Relevant bibliographies by topics / Transformer Architecture

Academic literature on the topic 'Transformer Architecture'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Transformer Architecture"

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Rahali, Abir, and Moulay A. Akhloufi. "End-to-End Transformer-Based Models in Textual-Based NLP." AI 4, no. 1 (January 5, 2023): 54–110. http://dx.doi.org/10.3390/ai4010004.

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Transformer architectures are highly expressive because they use self-attention mechanisms to encode long-range dependencies in the input sequences. In this paper, we present a literature review on Transformer-based (TB) models, providing a detailed overview of each model in comparison to the Transformer’s standard architecture. This survey focuses on TB models used in the field of Natural Language Processing (NLP) for textual-based tasks. We begin with an overview of the fundamental concepts at the heart of the success of these models. Then, we classify them based on their architecture and training mode. We compare the advantages and disadvantages of popular techniques in terms of architectural design and experimental value. Finally, we discuss open research, directions, and potential future work to help solve current TB application challenges in NLP.
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Chi, Ye, Haikun Liu, Ganwei Peng, Xiaofei Liao, and Hai Jin. "Transformer: An OS-Supported Reconfigurable Hybrid Memory Architecture." Applied Sciences 12, no. 24 (December 18, 2022): 12995. http://dx.doi.org/10.3390/app122412995.

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Non-volatile memories (NVMs) have aroused vast interest in hybrid memory systems due to their promising features of byte-addressability, high storage density, low cost per byte, and near-zero standby energy consumption. However, since NVMs have limited write endurance, high write latency, and high write energy consumption, it is still challenging to directly replace traditional dynamic random access memory (DRAM) with NVMs. Many studies propose to utilize NVM and DRAM in a hybrid memory system, and explore sophisticated memory management schemes to alleviate the impact of slow NVM on the performance of applications. A few studies architected DRAM and NVM in a cache/memory hierarchy. However, the storage and performance overhead of the cache metadata (i.e., tags) management is rather expensive in this hierarchical architecture. Some other studies architected NVM and DRAM in a single (flat) address space to form a parallel architecture. However, the hot page monitoring and migration are critical for the performance of applications in this architecture. In this paper, we propose Transformer, an OS-supported reconfigurable hybrid memory architecture to efficiently use DRAM and NVM without redesigning the hardware architecture. To identify frequently accessed (hot) memory pages for migration, we propose to count the number of page accesses in OSes by sampling the access bit of pages periodically. We further migrate the identified hot pages from NVM to DRAM to improve the performance of hybrid memory system. More importantly, Transformer can simulate a hierarchical hybrid memory architecture while DRAM and NVM are physically managed in a flat address space, and can dynamically shift the logical memory architecture between parallel and hierarchical architectures according to applications’ memory access patterns. Experimental results show that Transformer can improve the application performance by 62% on average (up to 2.7×) compared with an NVM-only system, and can also improve performance by up to 79% and 42% (21% and 24% on average) compared with hierarchical and parallel architectures, respectively.
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Cui, Liyuan, Guoqiang Zhong, Xiang Liu, and Hongwei Xu. "A Compact Object Detection Architecture with Transformer Enhancing." Journal of Physics: Conference Series 2278, no. 1 (May 1, 2022): 012034. http://dx.doi.org/10.1088/1742-6596/2278/1/012034.

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Abstract With the advancements in rising computer vision processing, Transformer has attracted increasing interesting in this field. However, it is limited because of its unprecedented storage, heavy reliance on data size and intolerable computational power consumption. While lightweight network is in other extreme, pursuing the compact architectures accompanied by performance loss. In this paper, we enhance an architecture as the backbone of object detection networks through combining right-size Transformer, i.e. Vision Transformer module. Specifically, based on GhostNet, a well-known lightweight neural network structure moreover, embed this Vision Transformer module at the end of GhostNet, and use the input data with slicing design to reduce the computational burden of the neural networks. Vision Transformer is taken to enhance the architecture as the backbone of object detection networks, and the well-known YOLOv5 as the baseline. We conduct multi-metric comparison experiments on two medium-scale object detection datasets with large, medium and small scale networks. Results show that without relying on ultra-large dataset and pre-trained models, the proposed Transformer module enhanced architecture achieves comparable or even higher mAP metrics with only half of the model size and floating-point computation of the baseline.
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Lorenzo, Javier, Ignacio Parra Alonso, Rubén Izquierdo, Augusto Luis Ballardini, Álvaro Hernández Saz, David Fernández Llorca, and Miguel Ángel Sotelo. "CAPformer: Pedestrian Crossing Action Prediction Using Transformer." Sensors 21, no. 17 (August 24, 2021): 5694. http://dx.doi.org/10.3390/s21175694.

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Anticipating pedestrian crossing behavior in urban scenarios is a challenging task for autonomous vehicles. Early this year, a benchmark comprising JAAD and PIE datasets have been released. In the benchmark, several state-of-the-art methods have been ranked. However, most of the ranked temporal models rely on recurrent architectures. In our case, we propose, as far as we are concerned, the first self-attention alternative, based on transformer architecture, which has had enormous success in natural language processing (NLP) and recently in computer vision. Our architecture is composed of various branches which fuse video and kinematic data. The video branch is based on two possible architectures: RubiksNet and TimeSformer. The kinematic branch is based on different configurations of transformer encoder. Several experiments have been performed mainly focusing on pre-processing input data, highlighting problems with two kinematic data sources: pose keypoints and ego-vehicle speed. Our proposed model results are comparable to PCPA, the best performing model in the benchmark reaching an F1 Score of nearly 0.78 against 0.77. Furthermore, by using only bounding box coordinates and image data, our model surpasses PCPA by a larger margin (F1=0.75 vs. F1=0.72). Our model has proven to be a valid alternative to recurrent architectures, providing advantages such as parallelization and whole sequence processing, learning relationships between samples not possible with recurrent architectures.
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Shao, Ran, Xiao-Jun Bi, and Zheng Chen. "A novel hybrid transformer-CNN architecture for environmental microorganism classification." PLOS ONE 17, no. 11 (November 11, 2022): e0277557. http://dx.doi.org/10.1371/journal.pone.0277557.

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The success of vision transformers (ViTs) has given rise to their application in classification tasks of small environmental microorganism (EM) datasets. However, due to the lack of multi-scale feature maps and local feature extraction capabilities, the pure transformer architecture cannot achieve good results on small EM datasets. In this work, a novel hybrid model is proposed by combining the transformer with a convolution neural network (CNN). Compared to traditional ViTs and CNNs, the proposed model achieves state-of-the-art performance when trained on small EM datasets. This is accomplished in two ways. 1) Instead of the original fixed-size feature maps of the transformer-based designs, a hierarchical structure is adopted to obtain multi-scale feature maps. 2) Two new blocks are introduced to the transformer’s two core sections, namely the convolutional parameter sharing multi-head attention block and the local feed-forward network block. The ways allow the model to extract more local features compared to traditional transformers. In particular, for classification on the sixth version of the EM dataset (EMDS-6), the proposed model outperforms the baseline Xception by 6.7 percentage points, while being 60 times smaller in parameter size. In addition, the proposed model also generalizes well on the WHOI dataset (accuracy of 99%) and constitutes a fresh approach to the use of transformers for visual classification tasks based on small EM datasets.
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Ibrahem, Hatem, Ahmed Salem, and Hyun-Soo Kang. "RT-ViT: Real-Time Monocular Depth Estimation Using Lightweight Vision Transformers." Sensors 22, no. 10 (May 19, 2022): 3849. http://dx.doi.org/10.3390/s22103849.

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The latest research in computer vision highlighted the effectiveness of the vision transformers (ViT) in performing several computer vision tasks; they can efficiently understand and process the image globally unlike the convolution which processes the image locally. ViTs outperform the convolutional neural networks in terms of accuracy in many computer vision tasks but the speed of ViTs is still an issue, due to the excessive use of the transformer layers that include many fully connected layers. Therefore, we propose a real-time ViT-based monocular depth estimation (depth estimation from single RGB image) method with encoder-decoder architectures for indoor and outdoor scenes. This main architecture of the proposed method consists of a vision transformer encoder and a convolutional neural network decoder. We started by training the base vision transformer (ViT-b16) with 12 transformer layers then we reduced the transformer layers to six layers, namely ViT-s16 (the Small ViT) and four layers, namely ViT-t16 (the Tiny ViT) to obtain real-time processing. We also try four different configurations of the CNN decoder network. The proposed architectures can learn the task of depth estimation efficiently and can produce more accurate depth predictions than the fully convolutional-based methods taking advantage of the multi-head self-attention module. We train the proposed encoder-decoder architecture end-to-end on the challenging NYU-depthV2 and CITYSCAPES benchmarks then we evaluate the trained models on the validation and test sets of the same benchmarks showing that it outperforms many state-of-the-art methods on depth estimation while performing the task in real-time (∼20 fps). We also present a fast 3D reconstruction (∼17 fps) experiment based on the depth estimated from our method which is considered a real-world application of our method.
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Lee, Jaewoo, Sungjun Lee, Wonki Cho, Zahid Ali Siddiqui, and Unsang Park. "Vision Transformer-Based Tailing Detection in Videos." Applied Sciences 11, no. 24 (December 7, 2021): 11591. http://dx.doi.org/10.3390/app112411591.

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Tailing is defined as an event where a suspicious person follows someone closely. We define the problem of tailing detection from videos as an anomaly detection problem, where the goal is to find abnormalities in the walking pattern of the pedestrians (victim and follower). We, therefore, propose a modified Time-Series Vision Transformer (TSViT), a method for anomaly detection in video, specifically for tailing detection with a small dataset. We introduce an effective way to train TSViT with a small dataset by regularizing the prediction model. To do so, we first encode the spatial information of the pedestrians into 2D patterns and then pass them as tokens to the TSViT. Through a series of experiments, we show that the tailing detection on a small dataset using TSViT outperforms popular CNN-based architectures, as the CNN architectures tend to overfit with a small dataset of time-series images. We also show that when using time-series images, the performance of CNN-based architecture gradually drops, as the network depth is increased, to increase its capacity. On the other hand, a decreasing number of heads in Vision Transformer architecture shows good performance on time-series images, and the performance is further increased as the input resolution of the images is increased. Experimental results demonstrate that the TSViT performs better than the handcrafted rule-based method and CNN-based method for tailing detection. TSViT can be used in many applications for video anomaly detection, even with a small dataset.
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He, Ju, Jie-Neng Chen, Shuai Liu, Adam Kortylewski, Cheng Yang, Yutong Bai, and Changhu Wang. "TransFG: A Transformer Architecture for Fine-Grained Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (June 28, 2022): 852–60. http://dx.doi.org/10.1609/aaai.v36i1.19967.

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Fine-grained visual classification (FGVC) which aims at recognizing objects from subcategories is a very challenging task due to the inherently subtle inter-class differences. Most existing works mainly tackle this problem by reusing the backbone network to extract features of detected discriminative regions. However, this strategy inevitably complicates the pipeline and pushes the proposed regions to contain most parts of the objects thus fails to locate the really important parts. Recently, vision transformer (ViT) shows its strong performance in the traditional classification task. The self-attention mechanism of the transformer links every patch token to the classification token. In this work, we first evaluate the effectiveness of the ViT framework in the fine-grained recognition setting. Then motivated by the strength of the attention link can be intuitively considered as an indicator of the importance of tokens, we further propose a novel Part Selection Module that can be applied to most of the transformer architectures where we integrate all raw attention weights of the transformer into an attention map for guiding the network to effectively and accurately select discriminative image patches and compute their relations. A contrastive loss is applied to enlarge the distance between feature representations of confusing classes. We name the augmented transformer-based model TransFG and demonstrate the value of it by conducting experiments on five popular fine-grained benchmarks where we achieve state-of-the-art performance. Qualitative results are presented for better understanding of our model.
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Gao, Shuguo, Jun Zhao, Yunpeng Liu, Ziqiang Xu, Zhe Li, Lu Sun, and Yuan Tian. "Research into Power Transformer Health Assessment Technology Based on Uncertainty of Information and Deep Architecture Design." Mathematical Problems in Engineering 2021 (April 2, 2021): 1–12. http://dx.doi.org/10.1155/2021/8831872.

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The uncertainty of the evaluation information is likely to affect the accuracy of the evaluation, when conducting a health evaluation of a power transformer. A multilevel health assessment method for power transformers is proposed in view of the three aspects of indicator criterion uncertainty, weight uncertainty, and fusion uncertainty. Firstly, indicator selection is conducted through the transformer guidelines and engineering experience to establish a multilevel model of transformers that can reflect the defect type and defect location. Then, a Gaussian cloud model is used to solve the uncertainty of the indicator criterion boundary. Based on association rules, AHP, and variable weights, the processed weights are calculated from the update module to obtain comprehensive weights, which overcomes the uncertainty of the weights. Improved DSmT theory is used for multiple evidence fusion to solve the high conflict and uncertainty problems in the fusion process. Finally, through actual case analysis, the defect type, defect location, and overall state of the transformer of the device are obtained. By comparing with many defect cases in a case-study library, the evaluation accuracy rate is found to reach 96.21%, which verifies the practicability and efficiency of the method.
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Xu, Zhen, David R. So, and Andrew M. Dai. "MUFASA: Multimodal Fusion Architecture Search for Electronic Health Records." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (May 18, 2021): 10532–40. http://dx.doi.org/10.1609/aaai.v35i12.17260.

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One important challenge of applying deep learning to electronic health records (EHR) is the complexity of their multimodal structure. EHR usually contains a mixture of structured (codes) and unstructured (free-text) data with sparse and irregular longitudinal features -- all of which doctors utilize when making decisions. In the deep learning regime, determining how different modality representations should be fused together is a difficult problem, which is often addressed by handcrafted modeling and intuition. In this work, we extend state-of-the-art neural architecture search (NAS) methods and propose MUltimodal Fusion Architecture SeArch (MUFASA) to simultaneously search across multimodal fusion strategies and modality-specific architectures for the first time. We demonstrate empirically that our MUFASA method outperforms established unimodal NAS on public EHR data with comparable computation costs. In addition, MUFASA produces architectures that outperform Transformer and Evolved Transformer. Compared with these baselines on CCS diagnosis code prediction, our discovered models improve top-5 recall from 0.88 to 0.91 and demonstrate the ability to generalize to other EHR tasks. Studying our top architecture in depth, we provide empirical evidence that MUFASA's improvements are derived from its ability to both customize modeling for each modality and find effective fusion strategies.
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Dissertations / Theses on the topic "Transformer Architecture"

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Ogunnika, Olumuyiwa Temitope 1978. "A simple transformer-based resonator architecture for low phase noise LC oscillators." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/28338.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2004.
Includes bibliographical references (leaves 86-87).
This thesis investigates the use of a simple transformer-coupled resonator to increase the loaded Q of a LC resonant tank. The windings of the integrated transformer replace the simple inductors as the inductive elements of the resonator. The resonator topology considered in this project is a simpler alternative to another proposed by Straayer et al [5] because it just requires a single varactor. A prime objective of this project is to prove that a transformer-coupled resonator which is simpler than that proposed by Straayer in [5] produces the same reduction in phase noise. The use of this type of resonator topology is a valuable technique which can be employed by RF engineers to reduce the phase noise generated by oscillators in high speed RF systems. Such techniques which increase the loaded Q of the resonator are very useful in practice because of the inverse squared relationship between resonator Q and the phase noise in the output signals of LC oscillators. The important aspect of this technique is that magnetic coupling between the windings of an integrated transformer increases their effective inductance while leaving their series resistance relatively unchanged. As a result, the Q of these inductive elements is increased and the phase noise generated by the oscillator is reduced. SpectreRF simulations of an LC oscillator with a center frequency of 5GHz were used to verify the performance of the proposed transformer-coupled resonator.
by Olumuyiwa Temitope Ogunnika.
S.M.
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Maneikis, Andrius. "Distribution On Load Tap Changer Control Using IEC61850 Client/Server Architecture." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-193673.

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Distributed generation is transforming the power system grid to decentralized system where separate units like wind power generators or solar panel shall coexist and operate in tandem in order to supplement each other and make one extensive system as a whole so called smart grid. It is utmost important to have a control ability over such units not only on a field level but on a system level as well. To be able to communicate with numerous devices and maintain interoperability universal standard is a must. Therefore, one of the core standards relevant to smart grids is IEC 61850 – Power Utility Automation which comes into assistance and tackles aforementioned challenges. This project uses IEC61850 architecture to implement client/server windows applications for on-load tap changer remote control. The proposed solution and designed applications are tested together with a real time simulator where simple power system is modelled to emulate the system response to control signals in a real time. In this way, the implemented applications can be tried and assessed as if performing in real environment. Consequently, a user of the client application is able to remotely control voltage on the power transformer's secondary side and manipulate the switching equipment simulated in the model.
Distribuerad generation håller på att förändra transmissionsnätet till decentraliserat system där separata enheter som vindkraftverk eller solpanel skall samexistera och fungera tillsammans för att komplettera varandra och att göra ett omfattande system som helhet så kallade smarta elnät. Det är ytterst viktigt att ha en kontroll förmåga över sådana enheter inte bara på ett fältnivå utan även på systemnivå. För att kunna kommunicera med många enheter och bibehålla interoperabiliten som universell standard är ett måste. En av de grundläggande normer som är relevanta för smarta nät är IEC 61850 - Skydd & Automation, som kommer in i bistånd och möter ovan nämnda utmaningar. Detta projekt använder IEC61850-struktur för att implementera klient/server windows applikation för lindningskopplarens fjärrkontroll. Den föreslagna lösningen och utformade applikationer testas tillsammans med en realtidssimulator där enkelt kraftsystem modelleras för att emulera systemets svar på de givna styrsignalerna i realtid. På detta sätt kan de implementerade programmen prövas och bedömas hur de utföras i verklig miljö. Följaktligen kan användare av klientapplikationen fjärrstyra spänningen på transformatorns sekundärsida och manipulera ställverk som simuleras i modellen.
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Policarpi, Andrea. "Transformers architectures for time series forecasting." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25005/.

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Time series forecasting is an important task related to countless applications, spacing from anomaly detection to healthcare problems. The ability to predict future values of a given time series is a non­trivial operation, whose complexity heavily depends on the number and the quality of data available. Historically, the problem has been addressed by statistical models and simple deep learning architectures such as CNNs and RNNs; recently many Transformer-based models have also been used, with excellent results. This thesis work aims to evaluate the performances of two transformer-based models, namely a TransformerT2V and an Informer, when applied to time series forecasting problems, and compare them with non-transformer architectures. Furthermore, a second contribution resides in the exploration of the Informer's Probsparse mechanism, and the suggestion of improvements to increase the model performances.
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Grzeszczak, Aleksander. "VLSI architecture for Discrete Wavelet Transform." Thesis, University of Ottawa (Canada), 1995. http://hdl.handle.net/10393/9908.

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In this thesis, we present a new simple and efficient VLSI architecture (DWT-SA) for computing the Discrete Wavelet Transform. The proposed architecture is systolic in nature, modular and extendible to 1-D or 2-D DWT transform of any size. The DWT-SA has been designed, simulated and implemented in silicon. The following are the features of the DWT-SA architecture: (1) It has an efficient (close to 100%) hardware utilization. (2) It works with data streams of arbitrary size. (3) The design is cascadable, for computation of one, two or three dimensional DWT. (4) It requires a minimum interface circuitry on the chip for purposes of interconnecting to a standard communication bus. The DWT-SA design has been implemented using CMOS 1.2 um technology.
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Ferreira, Costa Levy [Verfasser]. "Modular Power Converters for Smart Transformer Architectures / Levy Ferreira Costa." Kiel : Universitätsbibliothek Kiel, 2019. http://d-nb.info/1197055312/34.

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Skerry, Nathaniel S. (Nathaniel Standish) 1971. "Transformed materials : a material research center in Milan, Italy." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/70358.

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Thesis (M. Arch.)--Massachusetts Institute of Technology, Dept. of Architecture, 2002.
Includes bibliographical references (p. 74-75).
[Transformed Materials] is an exploration into today's design methodologies of architecture production. The emergence of architectural form is questioned in relation to the temporal state of design intent and the physical material construct. At a time when there is an increased awareness of the current state of technology, material innovation and methods of fabrication, there are new speculations of what materiality is and can be. This thesis will propose an architecture that emerges through an exploration of the material concept that directly informs and expresses the fundamental ideas of the project. Building methods have changed widely over time, and are co-responsible for creating a dialog between functional requirements, technological invention, and material implication that reflects the current cultural state. Today's architectural products have in a sense reverted back to thin surfaces. Current cultural issues such as socioeconomic, environmental impact, transportability, efficiency, lightness, storability, technology, and mass production, have over time created a state of "thinness ". This project tries to offset the current trend of building by accepting the norms of architectural products, and reinventing their role within a contemporary language that explores more deeply the material qualities and properties associates with it. This thesis will use steel as the primary building material. Steel is a material that has become standardized in how it is shaped and formed, thus its ability to produce an architecture has been reduced purely to a dogmatiC approach of engineered solutions or preconceived results. Steel, is artificial by nature; if we suspend our preconceptions of steel, could the material be designed such that its role is critical in defining space, structure and program in a tectonic system? The area of research and examination will be focused on the design of a Material Research Center (mRC). located in Milan, Italy.
by Nathaniel S. Skerry.
M.Arch.
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Mandly, Clayton E. "Push Pull - Compulsory Interactions in Architecture." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1428065491.

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Palmer, Joseph McRae. "The Hybrid Architecture Parallel Fast Fourier Transform (HAPFFT) /." Diss., CLICK HERE for online access, 2005. http://contentdm.lib.byu.edu/ETD/image/etd855.pdf.

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Palmer, Joseph M. "The Hybrid Architecture Parallel Fast Fourier Transform (HAPFFT)." BYU ScholarsArchive, 2005. https://scholarsarchive.byu.edu/etd/555.

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The FFT is an efficient algorithm for computing the DFT. It drastically reduces the cost of implementing the DFT on digital computing systems. Nevertheless, the FFT is still computationally intensive, and continued technological advances of computers demand larger and faster implementations of this algorithm. Past attempts at producing high-performance, and small FFT implementations, have focused on custom hardware (ASICs and FPGAs). Ultimately, the most efficient have been single-chipped, streaming I/O, pipelined FFT architectures. These architectures increase computational concurrency through the use of hardware pipelining. Streaming I/O, pipelined FFT architectures are capable of accepting a single data sample every clock cycle. In principle, the maximum clock frequency of such a circuit is limited only by its critical delay path. The delay of the critical path may be decreased by the addition of pipeline registers. Nevertheless this solution gives diminishing returns. Thus, the streaming I/O, pipelined FFT is ultimately limited in the maximum performance it can provide. Attempts have been made to map the Parallel FFT algorithm to custom hardware. Yet, the Parallel FFT was formulated and optimized to execute on a machine with multiple, identical, processing elements. When executed on such a machine, the FFT requires a large expense on communications. Therefore, a direct mapping of the Parallel FFT to custom hardware results in a circuit with complex control and global data movement. This thesis proposes the Hybrid Architecture Parallel FFT (HAPFFT) as an alternative. The HAPFFT is an improved formulation for building Parallel FFT custom hardware modules. It provides improved performance, efficient resource utilization, and reduced design time. The HAPFFT is modular in nature. It includes a custom front-end parallel processing unit which produces intermediate results. The intermediate results are sent to multiple, independent FFT modules. These independent modules form the back-end of the HAPFFT, and are generic, meaning that any prexisting FFT architecture may be used. With P back-end modules a speedup of P will be achieved, in comparison to an FFT module composed solely of a single module. Furthermore, the HAPFFT defines the front-end processing unit as a function of P. It hides the high communication costs typically seen in Parallel FFTs. Reductions in control complexity, memory demands, and logical resources, are achieved. An extraordinary result of the HAPFFT formulation is a sublinear area-time growth. This phenomenon is often also called superlinear speedup. Sublinear area-time growth and superlinear speedup are equivalent terms. This thesis will subsequently use the term superlinear speedup to refer to the HAPFFT's outstanding speedup behavior. A further benefit resulting from the HAPFFT formulation is reduced design time. Because the HAPFFT defines only the front-end module, and because the back-end parallel modules may be composed of any preexisting FFT modules, total design time for a HAPFFT is greatly reduced
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Camarda, Florent. "Développement d'une architecture reconfigurable pour transformée de Fourier rapide." Rennes, INSA, 2012. http://www.theses.fr/2012ISAR0036.

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Ce rapport traite du développement d'une architecture de Transformée de Fourier Rapide (TFR) reconfigurable pouvant répondre à la plupart des besoins nécessaires aux systèmes de communications numériques employant les modulations multiporteuses de type OFDM. Le premier chapitre rappelle la théorie du signal OFDM de base implique l'utilisation d'une Transformée de Fourier Discrète Inverse (TFDI) à l'émission et d'une Transformée de Fourier Discrète Directe (TFD) en réception. Cette opération constitue alors la plus importante complexité de réalisation matérielle donnant ainsi une importance évidente à son optimisation. Ce chapitre met également en avant le fait que bon nombre d'évolutions de ce type de modulation impliquent l'utilisation de TFD supplémentaires. Le second chapitre a pour but d'apporter une exploration non exhaustive des architectures existantes capable de traiter ces opérations. Des généralités sur les concepts architecturaux sont alors présentées ainsi que les différentes possibilités en matière de reconfiguration de ces dernières. Ainsi différentes architectures existantes sont classifiées selon le type d'algorithme de TFR qu'elles sont capables de traiter mais également selon leur possibilité de reconfiguration. Le troisième chapitre traite quant à lui des deux algorithmes de TFR qui sont exploitables par l'architecture proposée. Après des généralités sur quelques principaux algorithmes et leur classification, les deux algorithmes du Radix et de Winograd (Winograd Fourier Transform Algorithm WFTA) sont étudiés en profondeur. Cette étude montre alors qu'une combinaison astucieuse de ces deux algorithmes permet de traiter un grand ensemble possible de tailles de TFR. Le chapitre quatre présente l'architecture proposée, principale contribution de cette thèse. Cette architecture s'articule autour de trois principaux éléments : le papillon reconfigurable, l'application des coefficients de rotation et les mémoires nécessaires au stockage des données. Deux propositions de configuration sont proposées permettant d'optimiser l'architecture en termes de débit ou de ressources utilisées. Le contrôle de ces éléments est également présenté afin de permettre son implantation matérielle. Le chapitre suivant traite de la représentation des nombres au sein de l'architecture proposée. Après avoir présenté les représentations les plus courantes que sont la représentation en virgule fixe et celle en virgule flottante, l'étude de la représentation des nombres au sein de l'architecture est présentée en virgule fixe. Cette étude conduit alors à des résultats de simulation sur les performances atteignables par l'architecture en termes de Rapport Signal sur Bruit de Quantification (RSBQ). Le dernier chapitre traite de l'application de cette architecture dans le contexte de Télévision Numérique Terrestre (TNT). On rappelle pour les trois standards de diffusion de la TNT que sont DMB-T, ISDB-T et DVB-T les décompositions possibles conduisant à l'estimation des ressources nécessaires. Après quoi une étude des contraintes temporelles liées au contexte de diffusion (donc temps réel) permettra de définir la fréquence minimale nécessaire au bon fonctionnement de l'architecture. Enfin une conclusion générale résumera les avantages et inconvénients de la solution proposée. Elle permettra également de recenser quelques ouvertures possibles à ces travaux
This manuscript deals with the implementation of a reconfigurable Fast Fourier Transform architecture. The architecture is suitable for almost all the digital communication systems h using OFDM modulation. The first chapter starts with a reminder of the OFDM signal theories for which an Inverse Discrete Fourier Transform (lOFT) is required on the transmitter side as well as a Direct Discrete Fourier Transform (DFT) on the receiver side. This operation involves one of the greatest complexities of hardware implementation so that its optimization has a great importance. This chapter also presents the multiple ways in which the DFT and its inverse can be used in advanced OFDM modulations and demodulations. The goal of the second chapter is to explore the different ways of implementing the FFT. First, some general principles of the digital architectures are presented as well as the reconfiguration possibilities. Then, several FFT architectures are compared and classified depending on the way the operation is computed and whether the architecture is reconfigurable or not. The third chapter presents two algorithms of Fast Fourier Transform (FFT) which are computable via the proposed architecture. After a quick presentation and classification of the main different FFT algorithms, both the Radix algorithm and the Winograd Fourier Transform Algorithm (WFTA) are studied in depth. This study leads to the possibility of a clever combination of these two algorithms allowing then to compute a larger group of FFT sizes. Chapter four presents the proposed architecture, which is the major contribution of this PhD thesis. This architecture involves three main elements: the reconfigurable butterfly, the twiddle factors computation and data storage in memories. Two configurations of the architecture are proposed. The first allows high throughput for the results of the operation while the second one is optimized in resources use and power consumption. A control architecture for the computation units is also presented. The following chapter introduces the issue of number representations in hardware descriptions. The two most important data representation forms (fixed point and floating point) are presented. As the fixed-point representation is retained in order to reduce the hardware complexity, a study presents the impact of the imperfect representation of real values. Results are given in order to choose the best compromises between the computing precision and the resources needs. These results are given in terms of Signal to Quantization Noise Ratio for several given data widths. The last chapter provides results of the implementation of the architecture for the Digital Terrestrial Television Broadcasting (DTIB) application. Lt first evokes the main features of the three DTIB standards that are considered (DVB, DMB and ISDB). Then the implementations parameters are presented for the two configurations presented in the fourth chapter. Results are then given in terms of resources use for many data widths as well as for time performances. A general conclusion sums up all the results and lists all the advantages and drawbacks of the proposed architecture. Some perspectives are given in order to lead to many optimizations as well as an implementation on an Application Specifie lntegrated Circuit (ASIC) target
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Books on the topic "Transformer Architecture"

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Lavigne, Lucie. Construire, rénover, transformer: Les meilleures idées d'architectes pour réussir votre projet. Montréal: La Presse, 2011.

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Klanten, Robert. Build-on: Converted architecture and transformed buildings. Berlin: Gestalten, 2009.

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The Italian piazza transformed: Parma in the communal age. University Park, Pa: Pennsylvania State University Press, 2012.

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Davis, Douglas. The museum transformed: Design and culture in the post-Pompidou age. New York: Abbeville Press, 1990.

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France) Centre méridional de l'architecture et de la ville (Toulouse. Rêves de villes: Les habitants transforment leurs quartiers. [Toulouse]: Poïesis-AERA, 2007.

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The Highland house transformed: Architecture and identity on the edge of Britain, 1700-1850. Dundee: Dundee University Press, 2009.

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Cooper, Guy. Paradise transformed: The private garden for the twenty-first century. New York, N.Y: Monacelli Press, 1996.

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1949-, Clement Victoria Chave, ed. Stonlea: A timeworn, gilded age survivor transformed. Peterborough, New Hampshire: Bauhan Publishing, 2014.

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Hawes, Elizabeth. New York, New York: How the apartment house transformed the life of the city (1869-1930). New York: Knopf, 1993.

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Robinson, Cervin. Architecture transformed: A history of the photography of buildings from 1839 to the present. New York, N.Y: Architectural League of New York, 1987.

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Book chapters on the topic "Transformer Architecture"

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Su, Xiu, Shan You, Jiyang Xie, Mingkai Zheng, Fei Wang, Chen Qian, Changshui Zhang, Xiaogang Wang, and Chang Xu. "ViTAS: Vision Transformer Architecture Search." In Lecture Notes in Computer Science, 139–57. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19803-8_9.

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Huang, Zhaoyang, Xiaoyu Shi, Chao Zhang, Qiang Wang, Ka Chun Cheung, Hongwei Qin, Jifeng Dai, and Hongsheng Li. "FlowFormer: A Transformer Architecture for Optical Flow." In Lecture Notes in Computer Science, 668–85. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19790-1_40.

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Ni, Bolin, Gaofeng Meng, Shiming Xiang, and Chunhong Pan. "NASformer: Neural Architecture Search for Vision Transformer." In Lecture Notes in Computer Science, 47–61. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-02375-0_4.

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Wang, Xinyu, Ziwei Tang, Yaohua Yi, and Chaohua Gan. "Transformer-Based Coattention: Neural Architecture for Reading Comprehension." In Lecture Notes in Electrical Engineering, 548–55. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1864-5_75.

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Estupiñán-Ojeda, Cristian, Cayetano Guerra-Artal, and Mario Hernández-Tejera. "Informer: An Efficient Transformer Architecture Using Convolutional Layers." In Lecture Notes in Computer Science, 208–17. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10161-8_11.

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Yang, Chenyue, Yixuan Pan, Yichao Cao, and Xiaobo Lu. "CNN-Transformer Hybrid Architecture for Early Fire Detection." In Lecture Notes in Computer Science, 570–81. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-15937-4_48.

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Zhu, Wei, Xiaoling Wang, Yuan Ni, and Guotong Xie. "AutoTrans: Automating Transformer Design via Reinforced Architecture Search." In Natural Language Processing and Chinese Computing, 169–82. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88480-2_14.

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Rehman, Tohida, Raghubir Bose, Samiran Chattopadhyay, and Debarshi Kumar Sanyal. "Transfer Learning and Transformer Architecture for Financial Sentiment Analysis." In Algorithms for Intelligent Systems, 17–27. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1657-1_2.

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Gao, Yunhe, Mu Zhou, and Dimitris N. Metaxas. "UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, 61–71. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87199-4_6.

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Liu, Jihao, Xin Huang, Guanglu Song, Hongsheng Li, and Yu Liu. "UniNet: Unified Architecture Search with Convolution, Transformer, and MLP." In Lecture Notes in Computer Science, 33–49. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19803-8_3.

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Conference papers on the topic "Transformer Architecture"

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Wu, Xiao, Ting-Zhu Huang, Liang-Jian Deng, and Tian-Jing Zhang. "A Decoder-free Transformer-like Architecture for High-efficiency Single Image Deraining." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/205.

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Despite the success of vision Transformers for the image deraining task, they are limited by computation-heavy and slow runtime. In this work, we investigate Transformer decoder is not necessary and has huge computational costs. Therefore, we revisit the standard vision Transformer as well as its successful variants and propose a novel Decoder-Free Transformer-Like (DFTL) architecture for fast and accurate single image deraining. Specifically, we adopt a cheap linear projection to represent visual information with lower computational costs than previous linear projections. Then we replace standard Transformer decoder block with designed Progressive Patch Merging (PPM), which attains comparable performance and efficiency. DFTL could significantly alleviate the computation and GPU memory requirements through proposed modules. Extensive experiments demonstrate the superiority of DFTL compared with competitive Transformer architectures, e.g., ViT, DETR, IPT, Uformer, and Restormer. The code is available at https://github.com/XiaoXiao-Woo/derain.
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Sundfør, Ingolf, and Harald Selvær. "BIM as a Transformer of Processes." In CAADence in Architecture. Budapest University of Technology and Economics, Faculty of Architecture, 2016. http://dx.doi.org/10.3311/caadence.1690.

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Zhou, Qinqin, Kekai Sheng, Xiawu Zheng, Ke Li, Xing Sun, Yonghong Tian, Jie Chen, and Rongrong Ji. "Training-free Transformer Architecture Search." In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022. http://dx.doi.org/10.1109/cvpr52688.2022.01062.

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Zhao, Yuekai, Li Dong, Yelong Shen, Zhihua Zhang, Furu Wei, and Weizhu Chen. "Memory-Efficient Differentiable Transformer Architecture Search." In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.findings-acl.372.

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Ojeda, Cristian, Cayetano Artal, and Francisco Tejera. "Informer, an Information Organization Transformer Architecture." In 13th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010372703810389.

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Costa, Gabriel Sousa Silva, Anselmo C. Paiva, Geraldo Braz Júnior, and Marco Melo Ferreira. "COVID-19 automatic diagnosis with CT images using the novel Transformer architecture." In Simpósio Brasileiro de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/sbcas.2021.16073.

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Even though vaccines are already in use worldwide, the COVID-19 pandemic is far from over, with some countries re-establishing the lockdown state, the virus has taken over 2 million lives until today, being a serious health issue. Although real-time reverse transcription-polymerase chain reaction (RTPCR) is the first tool for COVID-19 diagnosis, its high false-negative rate and low sensitivity might delay accurate diagnosis. Therefore, fast COVID-19 diagnosis and quarantine, combined with effective vaccination plans, is crucial for the pandemic to be over as soon as possible. To that end, we propose an intelligent system to classify computed tomography (CT) of lung images between a normal, pneumonia caused by something other than the coronavirus or pneumonia caused by the coronavirus. This paper aims to evaluate a complete selfattention mechanism with a Transformer network to capture COVID-19 pattern over CT images. This approach has reached the state-of-the-art in multiple NLP problems and just recently is being applied for computer vision tasks. We combine vision transformer and performer (linear attention transformers), and also a modified vision transformer, reaching 96.00% accuracy.
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Choukroun, Yoni, and Lior Wolf. "Geometric Transformer for End-to-End Molecule Properties Prediction." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/401.

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Transformers have become methods of choice in many applications thanks to their ability to represent complex interactions between elements. However, extending the Transformer architecture to non-sequential data such as molecules and enabling its training on small datasets remains a challenge. In this work, we introduce a Transformer-based architecture for molecule property prediction, which is able to capture the geometry of the molecule. We modify the classical positional encoder by an initial encoding of the molecule geometry, as well as a learned gated self-attention mechanism. We further suggest an augmentation scheme for molecular data capable of avoiding the overfitting induced by the overparameterized architecture. The proposed framework outperforms the state-of-the-art methods while being based on pure machine learning solely, i.e. the method does not incorporate domain knowledge from quantum chemistry and does not use extended geometric inputs besides the pairwise atomic distances.
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Feng, Ben, Dayiheng Liu, and Yanan Sun. "Evolving transformer architecture for neural machine translation." In GECCO '21: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3449726.3459441.

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Rao, Pritam, Ashutosh Naik, Chirag Rana, and Sunil Ghane. "Segmentation of Nuclei using Transformer based Architecture." In 2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2022. http://dx.doi.org/10.1109/icccnt54827.2022.9984414.

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Gonzalez, M. C., P. Alou, O. Garcia, J. A. Oliver, J. A. Cobos, and H. Visairo. "DC-DC transformer multiphase converter with transformer coupling for two-stage architecture." In 2010 IEEE Applied Power Electronics Conference and Exposition - APEC 2010. IEEE, 2010. http://dx.doi.org/10.1109/apec.2010.5433579.

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Reports on the topic "Transformer Architecture"

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Wu, An-Yeu, and K. J. Liu. Algorithm-Based Low-Power Transform Coding Architectures. Part 2. Logarithmic Complexity, Unified Architecture, and Finite-Precision Analysis. Fort Belvoir, VA: Defense Technical Information Center, January 1995. http://dx.doi.org/10.21236/ada445617.

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Wu, An-Yeu, and K. J. Liu. Algorithm-Based Low-Power Transform Coding Architectures. Part 1. The Multirate Approach. Fort Belvoir, VA: Defense Technical Information Center, January 1995. http://dx.doi.org/10.21236/ada445610.

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Unzeta, Bruno Bueno, Jan de Boer, Ruben Delvaeye, Bertrand Deroisy, Marc Fontoynont, Daniel Neves Pimenta, Per Reinhold, Sophie Stoffer, and Robert Weitlaner. Review of lighting and daylighting control systems. IEA SHC Task 61, February 2021. http://dx.doi.org/10.18777/ieashc-task61-2021-0003.

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There is a large number of control systems proposed either by lighting manufacturers or motor manufacturers for shading systems. In addition there are many other solutions proposed by specific manufacturers of Building Management Systems (BMS) or manufacturers of components to be installed in luminaires and switches, as well as in the electric lighting architecture (transformers, gateways to the internet, sensors, etc.). For many consumers -i.e.-the installer, the facility manager, or the final user (building occupant) – this forms a complex and dynamic market environment with high frequent changes, every year or even every month or day. In this report we aim to provide some basic strategic information, showing the status of the supply at the time this report was written (2019-2021). Although the market develops very fast, there are principles of controls which are rather independent of the progress of technology.
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Hovakimyan, Naira, Hunmin Kim, Wenbin Wan, and Chuyuan Tao. Safe Operation of Connected Vehicles in Complex and Unforeseen Environments. Illinois Center for Transportation, August 2022. http://dx.doi.org/10.36501/0197-9191/22-016.

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Autonomous vehicles (AVs) have a great potential to transform the way we live and work, significantly reducing traffic accidents and harmful emissions on the one hand and enhancing travel efficiency and fuel economy on the other. Nevertheless, the safe and efficient control of AVs is still challenging because AVs operate in dynamic environments with unforeseen challenges. This project aimed to advance the state-of-the-art by designing a proactive/reactive adaptation and learning architecture for connected vehicles, unifying techniques in spatiotemporal data fusion, machine learning, and robust adaptive control. By leveraging data shared over a cloud network available to all entities, vehicles proactively adapted to new environments on the proactive level, thus coping with large-scale environmental changes. On the reactive level, control-barrier-function-based robust adaptive control with machine learning improved the performance around nominal models, providing performance and control certificates. The proposed research shaped a robust foundation for autonomous driving on cloud-connected highways of the future.
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Dubcovsky, Jorge, Tzion Fahima, and Ann Blechl. Molecular characterization and deployment of the high-temperature adult plant stripe rust resistance gene Yr36 from wheat. United States Department of Agriculture, November 2013. http://dx.doi.org/10.32747/2013.7699860.bard.

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Stripe rust, caused by Puccinia striiformis f. sp. tritici is one of the most destructive fungal diseases of wheat. Virulent races that appeared within the last decade caused drastic cuts in yields. The incorporation of genetic resistance against this pathogen is the most cost-effective and environmentally friendly solution to this problem. However, race specific seedling resistance genes provide only a temporary solution because fungal populations rapidly evolve to overcome this type of resistance. In contrast, high temperature adult plant (HTAP) resistance genes provide a broad spectrum resistance that is partial and more durable. The cloning of the first wheat HTAP stripe rust resistance gene Yr36 (Science 2009, 323:1357), funded by our previous (2007-2010) BARD grant, provided us for the first time with an entry point for understanding the mechanism of broad spectrum resistance. Two paralogous copies of this gene are tightly linked at the Yr36 locus (WKS1 and WKS2). The main objectives of the current study were to characterize the Yr36 (WKS) resistance mechanism and to identify and characterize alternative WKSgenes in wheat and wild relatives. We report here that the protein coded by Yr36, designated WKS1, that has a novel architecture with a functional kinase and a lipid binding START domain, is localized to chloroplast. Our results suggest that the presence of the START domain may affect the kinase activity. We have found that the WKS1 was over-expressed on leaf necrosis in wheat transgenic plants. When the isolated WKS1.1 splice variant transcript was transformed into susceptible wheat it conferred resistance to stripe rust, but the truncated variant WKS1.2 did not confer resistance. WKS1.1 and WKS1.2 showed different lipid binding profiling. WKS1.1 enters the chloroplast membrane, while WKS1.2 is only attached outside of the chloroplast membrane. The ascorbate peroxidase (APX) activity of the recombinant protein of TmtAPXwas found to be reduced by WKS1.1 protein in vitro. The WKS1.1 mature protein in the chloroplast is able to phosphorylate TmtAPXprotein in vivo. WKS1.1 induced cell death by suppressing APX activity and reducing the ability of the cell to detoxify reactive oxygen. The decrease of APX activity reduces the ability of the plant to detoxify the reactive H2O2 and is the possible mechanism underlying the accelerated cell death observed in the transgenic plants overexpressing WKS1.1 and in the regions surrounding a stripe rust infection in the wheat plants carrying the natural WKS1.1 gene. WKS2 is a nonfunctional paralog of WKS1 in wild emmer wheat, probably due to a retrotransposon insertion close to the alternative splicing site. In some other wild relatives of wheat, such as Aegilops comosa, there is only one copy of this gene, highly similar to WKS2, which is lucking the retrotransposon insertion. WKS2 gene present in wheat and WKS2-Ae from A. showed a different pattern of alternative splice variants, regardless of the presence of the retrotransposon insertion. Susceptible Bobwhite transformed with WKS2-Ae (without retrotansposon insertion in intron10), which derived from Aegilops comosaconferred resistance to stripe rust in wheat. The expression of WKS2-Ae in transgenic plants is up-regulated by temperature and pathogen infection. Combination of WKS1 and WKS2-Ae shows improved stripe rust resistance in WKS1×WKS2-Ae F1 hybrid plants. The obtained results show that WKS1 protein is accelerating programmed cell death observed in the regions surrounding a stripe rust infection in the wheat plants carrying the natural or transgenic WKS1 gene. Furthermore, characterization of the epistatic interactions of Yr36 and Yr18 demonstrated that these two genes have additive effects and can therefore be combined to increase partial resistance to this devastating pathogen of wheat. These achievements may have a broad impact on wheat breeding efforts attempting to protect wheat yields against one of the most devastating wheat pathogen.
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Kapulnik, Yoram, Maria J. Harrison, Hinanit Koltai, and Joseph Hershenhorn. Targeting of Strigolacatones Associated Pathways for Conferring Orobanche Resistant Traits in Tomato and Medicago. United States Department of Agriculture, July 2011. http://dx.doi.org/10.32747/2011.7593399.bard.

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This proposal is focused on examination of two plant interactions: parasitic with Orobanche, and symbiosis with arbuscular mycorrhiza fungi (AMF), and the involvement of a newly define plant hormones, strigolactones (SLs), in these plant interactions. In addition to strigolactones role in regulation of above-ground plant architecture, they are also known to be secreted from roots, and to be a signal for seed germination of the parasitic plants Orobanche. Moreover, secreted strigolactones were recognized as inducers of AMFhyphae branching. The present work was aimed at Generation of RNAi mutants of both tomato and Medicago, targeting multiple genes that may be involved in strigolactone production, carotenoid biosynthesis pathway, Pi signaling or other metabolic pathways, and hence affect AMF colonization and/or Orobanche resistance. Following the newly formed and existing RNAi mutants were examined for AMF colonization and Orobanche resistance. At the first phase of this project Orobanche seed germination assays and AMF colonization were examined in intact plants. These assays were shown to be effective and resulted with enhancement of Orobanche seed germination and AMF colonization in WT tomato plants, whereas roots of strigolactones impaired lines did not result with Orobanche seed germination and mycorrhiza colonization. Unexpectedly, root organ cultures (ROC) that were produced from the same wild type (WT) and mutant lines did not induce the Orobanche seed germination and AMFhyphal branching. This implies that under in vitro conditions ROC cultures are missing an important component for induction of Orobanche seed germination and AMFhyphal branching. In another line of experiments we have tested transgenic lines of Medicagotruncatula for AMFhuyphal branching and Orobanche seed germination assays. These lines included lines silenced for a GRAS transcription factor (RNAi 1845), an NBS-LRR type resistance gene (RNAi 1847), a kinase (RNAi 2403) and a protein of unknown function (RNAi 2417). In all cases, five independent transgenic root lines showed altered AMFphenotypes with reduced or aberrant colonization patterns. Following, we transformed tomato plants with the M. truncatulaTC 127050 PhosphoinositidekinaseRNAi construct. Transgenic lines that contained GUS constructs were used as control. All transgenic lines showed reduced level of Orobanche seed germination, masking any strigoalctones-specific effect. The research demonstrated that SLs production may not be examined in ROC –based bioassays. It was shown by the 3 independent assays employed in this project that none of the recognized characters of SLs may be reflected in these bioassays. However, when the whole plant root exudates were examined, SLs activity in root exudates was demonstrated. Hence, it can be concluded that the presence of an intact shoot, and possibly, shoot factors, may be necessary for production of SLs in roots. Another point of interest that rises from these results is that the presence of SLs is not necessary for AMF completion of life cycle. Hence, it may be concluded that SLs are important for AMFhyphal branching, before symbiosis, but not essential for AMF colonization and life cycle completion under ROC system conditions.
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