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Добірка наукової літератури з теми "Mechanism of attention"

Автор: Grafiati

Опубліковано: 11 березня 2023

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Статті в журналах з теми "Mechanism of attention"

Zang, Yubin, Zhenming Yu, Kun Xu, Minghua Chen, Sigang Yang, and Hongwei Chen. "Fiber communication receiver models based on the multi-head attention mechanism." Chinese Optics Letters 21, no. 3 (2023): 030602. http://dx.doi.org/10.3788/col202321.030602.

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Yoo, Sungwook, Hanjun Goo, and Kyuseok Shim. "Improving Review-based Attention Mechanism." KIISE Transactions on Computing Practices 27, no. 10 (October 31, 2021): 486–91. http://dx.doi.org/10.5626/ktcp.2021.27.10.486.

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Jia, Yuening. "Attention Mechanism in Machine Translation." Journal of Physics: Conference Series 1314 (October 2019): 012186. http://dx.doi.org/10.1088/1742-6596/1314/1/012186.

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Sieb, R. A. "A brain mechanism for attention." Medical Hypotheses 33, no. 3 (November 1990): 145–53. http://dx.doi.org/10.1016/0306-9877(90)90164-a.

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Park, Da-Sol, and Jeong-Won Cha. "Image Caption Generation using Object Attention Mechanism." Journal of KIISE 46, no. 4 (April 30, 2019): 369–75. http://dx.doi.org/10.5626/jok.2019.46.4.369.

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Spironelli, Chiara, Mariaelena Tagliabue, and Carlo Umiltà. "Response Selection and Attention Orienting." Experimental Psychology 56, no. 4 (January 2009): 274–82. http://dx.doi.org/10.1027/1618-3169.56.4.274.

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Recently, there has been a redirection of research efforts toward the exploration of the role of hemispheric lateralization in determining Simon effect asymmetries. The present study aimed at implementing a connectionist model that simulates the cognitive mechanisms implied by such asymmetries, focusing on the underlying neural structure. A left-lateralized response-selection mechanism was implemented alone (Experiment 1) or along with a right-lateralized automatic attention-orienting mechanism (Experiment 2). It was found that both models yielded Simon effect asymmetries. However, whereas the first model showed a reversed pattern of asymmetry compared with human, real data, the second model’s performance strongly resembled human Simon effect asymmetries, with a significantly greater right than left Simon effect. Thus, a left-side bias in the response-selection mechanism produced a left-side biased Simon effect, whereas a right-side bias in the attention system produced a right-side biased Simon effect. In conclusion, results showed that the bias of the attention system had a larger impact than the bias of the response-selection mechanism in producing Simon effect asymmetries.

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Songlin Yin, Songlin Yin, and Fei Tan Songlin Yin. "YOLOv4-A: Research on Traffic Sign Detection Based on Hybrid Attention Mechanism." 電腦學刊 33, no. 6 (December 2022): 181–92. http://dx.doi.org/10.53106/199115992022123306015.

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<p>Aiming at the problem of false detection and missed detection in the traffic sign detection task, an improved YOLOv4 detection algorithm is proposed. Based on the YOLOv4 algorithm, the Efficient Channel Attention Module (ECA) and the Convolutional Block Attention Module (CBAM) are added to form YOLOv4-A algorithm. At the same time, the global K-means clustering algorithm is used to regenerate smaller anchors, which makes the network converge faster and reduces the error rate. The YOLOv4-A algorithm re-calibrates the detection branch features in the two dimensions of channel and space, so that the network can focus and enhance the effective features, and suppress the interference features, which improves the detection ability of the algorithm. Experiments on the TT100K traffic sign dataset show that the proposed algorithm has a particularly significant improvement in the performance of small target detection. Compared with the YOLOv4 algorithm, the precision and mAP@0.5 of the proposed algorithm are increased by 5.38% and 5.75%.</p> <p> </p>

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Mao, Guojun, Guanyi Liao, Hengliang Zhu, and Bo Sun. "Multibranch Attention Mechanism Based on Channel and Spatial Attention Fusion." Mathematics 10, no. 21 (November 6, 2022): 4150. http://dx.doi.org/10.3390/math10214150.

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Recently, it has been demonstrated that the performance of an object detection network can be improved by embedding an attention module into it. In this work, we propose a lightweight and effective attention mechanism named multibranch attention (M3Att). For the input feature map, our M3Att first uses the grouped convolutional layer with a pyramid structure for feature extraction, and then calculates channel attention and spatial attention simultaneously and fuses them to obtain more complementary features. It is a “plug and play” module that can be easily added to the object detection network and significantly improves the performance of the object detection network with a small increase in parameters. We demonstrate the effectiveness of M3Att on various challenging object detection tasks, including PASCAL VOC2007, PASCAL VOC2012, KITTI, and Zhanjiang Underwater Robot Competition. The experimental results show that this method dramatically improves the object detection effect, especially for the PASCAL VOC2007, and the mapping index of the original network increased by 4.93% when embedded in the YOLOV4 (You Only Look Once v4) network.

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V, Ms Malge Shraddha. "Generating Image Descriptions using Attention Mechanism." International Journal for Research in Applied Science and Engineering Technology 9, no. 3 (March 31, 2021): 1047–56. http://dx.doi.org/10.22214/ijraset.2021.33397.

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Yakura, Hiromu, Shinnosuke Shinozaki, Reon Nishimura, Yoshihiro Oyama, and Jun Sakuma. "Neural malware analysis with attention mechanism." Computers & Security 87 (November 2019): 101592. http://dx.doi.org/10.1016/j.cose.2019.101592.

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Дисертації з теми "Mechanism of attention"

Fitzgerald, Marilyn. "Are attention bias and interpretation bias reflections of a single common mechanism or multiple independent mechanisms?" University of Western Australia. School of Psychology, 2008. http://theses.library.uwa.edu.au/adt-WU2009.0052.

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There is abundant evidence of anxiety-linked threat-biased attention and anxiety-linked threat-biased interpretation (cf. Mathews & MacLeod, 1994, 2005). The present research aimed to determine whether these cognitive biases reflect a single common underlying mechanism (the Common Mechanism Account) or multiple independent underlying mechanisms (the Independent Mechanisms Account). To address this question, a battery of eight experimental tasks was developed; four tasks measured attention bias and four measured interpretation bias. Participants with different levels of trait anxiety, completed pairs of these tasks. The pattern of associations amongst all eight tasks was compared with the pattern of associations between the four tasks that measured attention bias and the pattern of associations between the four tasks that measured interpretation bias. Both Accounts predicted strong associations between the four tasks that measured attention bias, and between the four tasks that measured interpretation bias. However, the Common Mechanism Account predicted generally strong associations between all of the eight tasks, that were equivalent in strength to the associations between tasks measuring attention bias and to the associations between tasks measuring interpretation bias. In contrast, the Independent Mechanisms Account predicted weaker associations between all of the eight tasks than the associations either between the tasks measuring attention bias or between the tasks measuring interpretation bias. The obtained pattern of associations between internally reliable measures of anxiety-linked attention bias and anxiety-linked interpretation bias failed to support the Common Mechanism Account, but rather was consistent with the predictions of the Independent Mechanisms Account. Theoretical and applied implications of the results are discussed.

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Yan, Shiyang. "Visual attention mechanism in deep learning and its applications." Thesis, University of Liverpool, 2018. http://livrepository.liverpool.ac.uk/3028892/.

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Recently, in computer vision, a branch of machine learning, called deep learning, has attracted high attention due to its superior performance in various computer vision tasks such as image classification, object detection, semantic segmentation, action recognition and image description generation. Deep learning aims at discovering multiple levels of distributed representations, which have been validated to be discriminatively powerful in many tasks. Visual attention is an ability of the vision system to selectively focus on the salient and relevant features in a visual scene. The core objective of visual attention is to achieve the least possible amount of visual information to be processed to solve the complex high-level tasks, e.g., object recognition, which can lead the whole vision process to become effective. The visual attention is not a new topic which has been addressed in the conventional computer vision algorithms for many years. The development and deployment of visual attention in deep learning algorithms are of vital importance since the visual attention mechanism matches well with the human visual system and also shows an improving effect in many real-world applications. This thesis is on the visual attention in deep learning, starting from the recent progress in visual attention mechanism, followed by several contributions on the visual attention mechanism targeting at diverse applications in computer vision, which include the action recognition from still images, action recognition from videos and image description generation. Firstly, the soft attention mechanism, which was initially proposed to combine with Recurrent Neural Networks (RNNs), especially the Long Short-term Memories (LSTMs), was applied in image description generation. In this thesis, instead, as one contribution to the visual attention mechanism, the soft attention mechanism is proposed to directly plug into the convolutional neural networks for the task of action recognition from still images. Specifically, a multi-branch attention network is proposed to capture the object that the human is intereating with and the scene in which the action is performing. The soft attention mechanism applying in this task plays a significant role in capturing multi-type contextual information during recognition. Also, the proposed model can be applied in two experimental settings: with and without the bounding box of the person. The experimental results show that the proposed networks achieved state-of-the-art performance on several benchmark datasets. For the action recognition from videos, our contribution is twofold: firstly, the hard attention mechanism, which selects a single part of features during recognition, is essentially a discrete unit in a neural network. This hard attention mechanism shows superior capacity in discriminating the critical information/features for the task of action recognition from videos, but is often with high variance during training, as it employs the REINFORCE algorithm as its gradient estimator. Hence, this brought another critical research question, i.e., the gradient estimation of the discrete unit in a neural network. In this thesis, a Gumbel-softmax gradient estimator is applied to achieve this goal, with much lower variance and more stable training. Secondly, to learn a hierarchical and multi-scale structure for the multi-layer RNN model, we embed discrete gates to control the information between each layer of the RNNs. To make the model differentiable, instead of using the REINFORCE-like algorithm, we propose to use Gumbel-sigmoid to estimate the gradient of these discrete gates. For the task of image captioning, there are two main contributions in this thesis: primarily, the visual attention mechanism can not only be used to reason on the global image features but also plays a vital role in the selection of relevant features from the fine-grained objects appear in the image. To form a more comprehensive image representation, as a contribution to the encoder network for image captioning, a new hierarchical attention network is proposed to fuse the global image and local object features through the construction of a hierarchical attention structure, to better the visual representation for the image captioning. Secondly, to solve an inherent problem called exposure-biased issue of the RNN-based language decoder commonly used in image captioning, instead of only relying on the supervised training scheme, an adversarial training-based policy gradient optimisation algorithm is proposed to train the networks for image captioning, with improved results on the evaluation metrics. In conclusion, comprehensive research has been carried out for the visual attention mechanism in deep learning and its applications, which include action recognition and image description generation. Related research topics have also been discussed, for example, the gradient estimation of the discrete units and the solution to the exposure-biased issue in the RNN-based language decoder. For the action recognition and image captioning, this thesis presents several contributions which proved to be effective in improving existing methods.

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Parker, Amanda Louise. "A cross-modal investigation into the relationships between bistable perception and a global temporal mechanism." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9545.

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When the two eyes are presented with sufficiently different images, Binocular Rivalry (BR) occurs. BR is a form of bistable perception involving stochastic alternations in awareness between distinct images shown to each eye. It has been suggested that the dynamics of BR are due to the activity of a central temporal process and are linked to involuntary mechanisms of selective attention (aka exogenous attention). To test these ideas, stimuli designed to evoke exogenous attention and central temporal processes were employed during BR observation. These stimuli included auditory and visual looming motion and streams of transient events of varied temporal rate and pattern. Although these stimuli exerted a strong impact over some aspects of BR, they were unable to override its characteristic stochastic pattern of alternations completely. It is concluded that BR is subject to distributed influences, but ultimately, is achieved in neural processing areas specific to the binocular conflict.

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Raykos, Bronwyn C. "Attentional and interpretive biases : independent dimensions of individual difference or expressions of a common selective processing mechanism?" University of Western Australia. School of Psychology, 2007. http://theses.library.uwa.edu.au/adt-WU2007.0018.

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[Truncated abstract] Attentional and interpretive biases are important dimensions of individual difference that have been implicated in the etiology and maintenance of a range of clinical problems. Yet there has been no systematic investigation into the relationship between these dimensions of individual difference. The current research program tested predictions derived from two competing theoretical accounts of the relationship between attentional and interpretive biases. The Common Mechanism Account proposes that cognitive biases represent concurrent manifestations of a single underlying selective processing mechanism. The Independent Mechanism account proposes that independent mechanisms underlie each bias. . . An apparent contradiction is that the manipulation of one bias served to also modify the other bias, despite the observation that the magnitude of the resulting change in both biases was uncorrelated. Neither the Common Mechanism nor the Independent Pathways accounts can adequately explain this pattern of results. A new account is proposed, in which attentional and interpretive biases are viewed as representing mechanisms that are related but that are not the same. Theoretical and applied implications of these findings are discussed, including the possibility that the two biases each may best predict emotional reactions to quite different stressful events and that training programs designed to attenuate allocation of attentional resources to threat may serve to reduce both attentional and interpretive selectivity in emotionally vulnerable individuals.

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Wang, Jing. "Hyperspectral Image Classification Based on Deep Learning and Module Inspired by Human Attention Mechanism." Thesis, Griffith University, 2020. http://hdl.handle.net/10072/397634.

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Hyperspectral imaging technology acquires image data in a number of continuous narrow bands of the electromagnetic wave. The obtained hyperspectral images contain details of spectral re ectance of targets in addition to spatial information. The ability to characterize abundant spectral details of hyperspectral image makes it particularly suitable for remote sensing image analysis. Hyperspectral remote sensing image classi cation is one of the most important applications in remote sensing, and is the main research problem of this thesis. Researchers have already proposed a large variety of methods for hyperspectral image classi cation in the last few decades, which can be categorized into traditional methods and deep learning based methods. Recently, with the development of high performance computing and collection of large datasets, deep learning methods have been state of the art in hyperspectral image classi cation. Most of the existing deep learning methods take in the hyperspectral image and learn discriminant features in plain convolutional or fully connected layers. This learning manner treats all raw pixels and extracted features equally. However, human brains do not perform recognition task with equal consideration of every involved element. For recognition or classi cation tasks, it is possible that some parts of inputs or features are more important, while others are useless. Our visual system has the capability of attending to the signi cant aspects and ignoring irrelevant components. This has greatly contributed to our cognition ability and e ciency. Inspired by the attention mechanism of human brain, we design corresponding attention modules in the context of arti cial neural network for hyperspectral image classi cation. In addition, human visual system is a universal feature extractor and classi er in the sense that we can perform classi cation across multiple image styles, modalities and distributions. On the contrary, current deep learning based hyperspectral classi - cation paradigms require an individual model for every data domain. This is expensive and ine cient. Following similar philosophy of attention mechanism, we design domain attention modules for multi-domain hyperspectral image classi cation. In this thesis, we propose three attention modules for deep learning based hyperspectral image classi cation. In the rst work, we introduce attention based feature weighting networks for improving the classi cation accuracy of current plain neural networks. In a deep network for hyperspectral application, a hierarchy of spectral or spatial features are extracted layer by layer. Each layer contains the same semantic level of features. To model the importance of features in the same level, attention modules are designed by branching from current feature maps. In the attention branch, three steps are executed: summarizing information from current layer, modeling relationship among the features with fully connected or convolution layers, and outputting weighting masks to be multiplied with the original features. We propose feature weighting attention modules for spectral CNN, spatial CNN and spectral-spatial CNN, respectively. In the second work, we design attention modules speci cally attending to the bands of hyperspectral image. Compared to hidden features extracted in hidden layers of neural networks which have less interpretability and physical meaning, spectral bands of hyperspectral images correspond directly to real wavelength in the physical world. Thus attending to bands has special importance in a couple of aspects. First, it in uences the design and cost of hyperspectral sensor. Second, it is directly related to the dimension of the obtained raw data. Our band attention module can perform both band weighting and band selection. For band weighting, it has the ability to assign sample-wise weights to hyperspectral images and can interfere with the feature learning process in the early stage. For band selection, we carefully design an additional parallel input to the attention module for obtaining xed selected band sets and an activation function for ltering insigni cant bands in the training process. In the third work, we propose attention mechanisms to address multi-domain hyperspectral image classi cation. Di erent hyperspectral datasets have di erent data modalities, statistical distributions, or spectral dimensionalities. This brings signi cant challenges for a single network to learn all the tasks. The domain shift problem can be alleviated by adjusting the network towards the property of speci c domains. To this end, domain attention modules are designed to attend to the domain of the input data for adapting the network accordingly. Two domain attention modules: hard domain attention and soft domain attention are proposed. For the hard domain attention network, the attention mechanism is implemented by a muxer switch. According to the labels of data domain, a set of small domain speci c adapters are selected and connected to a main backbone network. In this way, the majority of network parameters are shared by all domains with only a small number of domain speci c parameters. For the soft domain attention network, we build the attention mechanism based on squeeze and excitation (SE) block. Several parallel SE blocks are applied as the feature adapters. On top of them, a higher level domain attention SE block is placed to achieve domain assignment.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Info & Comm Tech
Science, Environment, Engineering and Technology
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DAL, MOLIN Anna. "Interaction between mechanism of attention selection in space and time: Behavioural and electrophysiological evidence." Doctoral thesis, Università degli Studi di Verona, 2009. http://hdl.handle.net/11562/337444.

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I meccanismi attentivi consentono di selezionare dall'ambiente circostante le informazioni utili allosvolgimento di un determinato compito. Negli ultimi trenta anni, i processi coinvolti nella selezionedi informazioni di natura spaziale sono stati ampiamente investigati, mentre rimangono ancora dachiarire i meccanismi coinvolti negli aspetti di selezione temporale. I tre esperimenti riportatiall'interno di questa tesi sono volti ad indagare alcuni degli aspetti legati alla capacità di selezionaregli eventi nel tempo ed in che modo gli aspetti temporali e quelli spaziali interagiscono tra loro.Nel primo esperimento è stato impiegato un compito di Giudizio di Ordine Temporale (TOJ) perinvestigare la relazione esistente tra disturbi di selezione nello spazio e nel tempo in pazienti coneminegligenza spaziale unilaterale. Una forte compromissione dei meccanismi di selezione neltempo è stata rilevata per le coppie di stimoli presentate in porzioni dello spazio in cui il deficitspaziale è più marcato, suggerendo l'esistenza una relazione tra gli aspetti spaziali e quelli temporalinella modulazione del deficit.Nel secondo e nel terzo esperimento è stato investigato l'orientamento dell'attenzione nel tempoutilizzando stimoli che, grazie ad un movimento con velocità regolare o irregolare, rendonopossibile il generarsi di aspettative temporali e di verificare cosa avviene quando tali aspettativevengono disattese. La regolarità del movimento si è rivelato essere un indice importante nelgenerare aspettative temporali che a loro volta influenzano profondamente la performancediminuendo sensibilmente la velocità di risposta del soggetto. Inoltre, la registrazione dei potenzialievocati ha evidenziato come aspettative spaziali e temporali interagiscano influenzando l'analisidello stimolo fin dalle prime fasi di elaborazione.
The study of mechanisms involved in spatial attention is one of the most investigated field inmodern neuroscience, but in the last years a growing interest has been devoted to unveil themechanisms concerning also the temporal aspects of attention. In this thesis three experiment arereported that tried to cast more light on the temporal aspects of attention and on the relationshipbetween spatial and temporal attentional mechanisms.In the first experiment the relationship between spatial and temporal deficit in selective visualattention has been investigated in a group of neglect patients using a temporal order judgement task(TOJ). The main finding is a stronger impairment in temporal selection for spatial position in whichthe attention selection is more impaired, suggesting an interaction between the two aspects in themodulation of the deficit.The second and the third experiment investigated temporal expectations generated by a regularrhythm. In particular, the impact of exogenous and endogenous temporal expectation has beencompared in a discrimination task, revealing the pervasive effect of regularity of movement andspeed in orienting attention in time. Moreover, it has been confirmed the combined effect of spatialand temporal expectations in modulation of electrophysiological response.These results suggest the existence of an interaction between spatial and temporal mechanisms ofattention.

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Isunza, Navarro Abgeiba Yaroslava. "Evaluation of Attention Mechanisms for Just-In-Time Software Defect Prediction." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-288724.

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Just-In-Time Software Defect Prediction (JIT-DP) focuses on predicting errors in software at change-level with the objective of helping developers identify defects while the development process is still ongoing, and improving the quality of software applications. This work studies deep learning techniques by applying attention mechanisms that have been successful in, among others, Natural Language Processing (NLP) tasks. We introduce two networks named Convolutional Neural Network with Bidirectional Attention (BACNN) and Bidirectional Attention Code Network (BACoN) that employ a bi-directional attention mechanism between the code and message of a software change. Furthermore, we examine BERT [17] and RoBERTa [57] attention architectures for JIT-DP. More specifically, we study the effectiveness of the aforementioned attention-based models to predict defective commits compared to the current state of the art, DeepJIT [37] and TLEL [101]. Our experiments evaluate the models by using software changes from the OpenStack open source project. The results showed that attention-based networks outperformed the baseline models in terms of accuracy in the different evaluation settings. The attention-based models, particularly BERT and RoBERTa architectures, demonstrated promising results in identifying defective software changes and proved to be effective in predicting defects in changes of new software releases.
Just-In-Time Defect Prediction (JIT-DP) fokuserar på att förutspå fel i mjukvara vid ändringar i koden, med målet att hjälpa utvecklare att identifiera defekter medan utvecklingsprocessen fortfarande är pågående, och att förbättra kvaliteten hos applikationsprogramvara. Detta arbete studerar djupinlärningstekniker genom att tillämpa attentionmekanismer som har varit framgångsrika inom, bland annat, språkteknologi (NLP). Vi introducerar två nätverk vid namn Convolutional Neural Network with Bidirectional Attention (BACNN), och Bidirectional Attention Code Network (BACoN), som använder en tvåriktad attentionmekanism mellan koden och meddelandet om en mjukvaruändring. Dessutom undersöker vi BERT [17] och RoBERTa [57], attentionarkitekturer för JIT-DP. Mer specifikt studerar vi hur effektivt dessa attentionbaserade modeller kan förutspå defekta ändringar, och jämför dem med de bästa tillgängliga arkitekturerna DeePJIT [37] och TLEL [101]. Våra experiment utvärderar modellerna genom att använda mjukvaruändringar från det öppna källkodsprojektet OpenStack. Våra resultat visar att attentionbaserade nätverk överträffar referensmodellen sett till träffsäkerheten i de olika scenarierna. De attentionbaserade modellerna, framför allt BERT och RoBERTa, demonstrerade lovade resultat när det kommer till att identifiera defekta mjukvaruändringar och visade sig vara effektiva på att förutspå defekter i ändringar av nya mjukvaruversioner.

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PUTELLI, LUCA. "Attention Mechanism e Interpretabilità del Deep Learning per il Natural Language Processing in Ambito Biomedico." Doctoral thesis, Università degli studi di Brescia, 2021. http://hdl.handle.net/11379/548259.

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Raykos, Bronwyn C. "Attentional and interpretive biases : independent dimensions of individual difference or expressions of a common selective processing mechanism? /." Connect to this title, 2006. http://theses.library.uwa.edu.au/adt-WU2007.0018.

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Ma, Tengfei. "A Graph Attention plus Reinforcement Learning Method for Antenna Tilt Optimization." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300111.

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Remote Electrical Tilt optimization is an effective method to obtain the optimal Key Performance Indicators (KPIs) by remotely controlling the base station antenna’s vertical tilt. To improve the KPIs aims to improve antennas’ cooperation effect since KPIs measure the quality of cooperation between the antenna to be optimized and its neighbor antennas. Reinforcement Learning (RL) is an appropriate method to learn an antenna tilt control policy since the agent in RL can generate the optimal epsilon greedy tilt optimization policy by observing the environment and learning from the state- action pairs. However, existing models only produced tilt modification strategies by interpreting the to- be- optimized antenna’s features, which cannot fully characterize the mobile cellular network formed by the to- be- optimized antenna and its neighbors. Therefore, incorporating the features of the neighboring antennas into the model is an important measure to improve the optimization strategy. This work will introduce the Graph Attention Network to model the neighborhood antenna’s impact on the antenna to be optimized through the attention mechanism. Furthermore, it will generate a low- dimensional embedding vector with more expressive power to represent the to- be- optimized antenna’s state in the RL framework through dealing with graph- structural data. This new model, namely Graph Attention Q- Network (GAQ), is a model based on DQN and aims to acquire a higher performance than the Deep Q- Network (DQN) model, which is the baseline, evaluated by the same metric — KPI Improvement. Since GAQ has a richer perception of the environment than the vanilla DQN model, it thereby outperforms the DQN model, obtaining fourteen percent performance improvement compared to the baseline. Besides, GAQ also performs 14 per cent better than DQN in terms of convergence efficiency.
Optimering av fjärrlutning är en effektiv metod för att nå optimala nyckeltal genom fjärrstyrning av den vertikala lutningen av en antenn i en basstation. Att förbättra nyckeltalen innebär att förbättra sammarbetseffekten mellan antenner eftersom nyckeltalen är mått på kvalitén av sammarbetet mellan den antenn som optimeras och dess angränsande antenner. Förstärkande Inlärning (FI) är en lämplig metod för att lära sig en optimal strategi för reglering av antennlutningen eftersom agenten inom FI kan generera den optimala epsilongiriga optimeringsstrategin genom att observera miljön och lära sig från par av tillstånd och aktioner. Nuvarande modeller genererar dock endast lutningsstrategier genom att tolka egenskaperna hos den antenn som ska optimeras, vilket inte är tillräckligt för att karatärisera mobilnätverket bestående av antennen som ska optimeras samt dess angränsande antenner. Därav är inkluderingen av de angränsande antennernas egenskaper i modellen viktig för att förbättra optimeringsstrategin. Detta arbete introducerar Graf- Uppmärksammat Nätverk för att modellera de angränsande antennernas påverkan på den antenn som ska optimeras genom uppmärksamhetsmekanismen. Metoden genererar en lågdimensionell vektor med större förmåga att representera den optimerade antennens tillstånd i FI modellen genom att hantera data i struktur av en graf. Den nya modellen, Graf- Uppmärksammat Q- Nätverk (GUQ), är en modell baserad på DQN med mål att nå bättre prestanda än en standard DQN- modell, utvärderat efter samma mätvärde –– förbättring av nyckeltalen. Eftersom GUQ har en större upfattning av miljön så överträffar metoden DQN- modellen genom en fjorton procent bättre prestandaökning. Dessutom, så överträffar GUQ även DQN i form av snabbare konvergens.

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Книги з теми "Mechanism of attention"

Glazkova, Mariya. Court practice in the mechanism of legal monitoring. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/25284.

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The manual discusses the role of judicial practice in the implementation of the mechanism of legal monitoring on the Federal, regional and local levels. It justifies significance of judicial practice as an integral part of the legal monitoring, since it is the judiciary, which is constant- Janno being at the turn of sometimes conflicting interests to have the most complete information about the quality of legislation. Describes the theoretical and normative foundations of legal monitoring, its organization and influence on the development of procedural law and the legal system. Special attention given the anti-corruption monitoring. The work is aimed at resolving issues of implementation of legal monitoring in the activities of public authorities, business-structures, public organizations and other civil society institutions in order to make informed proposals on optimization of the Russian legislation. For deputies, employees of state and municipal authorities, representatives of civil society institutions, scientific workers, teachers, postgraduates and students of law universities and faculties.

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Cantoni, Virginio, Maria Marinaro, and Alfredo Petrosino, eds. Visual Attention Mechanisms. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0111-4.

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V, Cantoni, Marinaro M, and Petrosino Alfredo, eds. Visual attention mechanisms. New York: Kluwer Academic/Plenum Publishers, 2002.

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Cantoni, V. Visual Attention Mechanisms. Boston, MA: Springer US, 2002.

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Samovich, Yuliya, and Ramil Sharifullin. International protection of human rights: universal mechanisms. ru: Publishing Center RIOR, 2021. http://dx.doi.org/10.29039/02042-5.

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The tutorial examines the universal mechanism for the international protection of individual rights, in particular the United Nations system and treaty bodies. Special attention is paid to the analysis of the effectiveness of the international control mechanism and proposals for its improvement. The content will allow students to both independently fill in the missed material and get acquainted with additional. The manual is recommended for students in the direction 40.04.01 "Jurisprudence" (magistracy), graduate students, specialty students.

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Sabine, Maasen, ed. Mechanisms of visual attention: A cognitive neuroscience perspective. East Sussex, UK: Psychology Press, 1998.

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Balynin, Igor', Natal'ya Vlasova, Aleksey Gubernatorov, Lyudmila Koreckaya, Dmitriy Kuznecov, Evgeniy Lomov, Tat'yana Nikerova, et al. Corporate finance. ru: INFRA-M Academic Publishing LLC., 2019. http://dx.doi.org/10.12737/1013023.

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The textbook deals with the organization of corporate management in the innovation and digital economy, describes the direction of investment policy and the mechanism of its implementation in the activities of the Corporation. Special attention is paid to the mechanisms and methods of financing the Corporation's activities, as well as applied aspects of modeling business processes and their effectiveness. Meets the requirements of the Federal state educational standards of higher education of the last generation. It is intended for students studying in the areas of "management" and "Economics", and can also be useful for graduate students and teachers.

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Bauer-Amin, Sabine, Leonardo Schiocchet, and Maria Six-Hohenbalken, eds. Embodied Violence and Agency in Refugee Regimes. Bielefeld, Germany: transcript Verlag, 2022. http://dx.doi.org/10.14361/9783839458020.

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Multiple refugee regimes govern the lives of forced migrants simultaneously but in an often conflicting way. As a mechanism of inclusion/exclusion, they tend to engender the violence they sought to dissipate. Protection and control channel agency through mechanisms of either tutelage and victimisation or criminalisation. This book contrasts multiple groups of refugees and refugee regimes, revealing the inherent coercive violence of refugee regimes, from displacement and expulsion, to stereotypification and exclusion in host countries, and academic knowledge essentialisation. This violence is international, national, society-based, internalised, and embodied - and it urgently needs due scholarly attention.

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Baruni, Jalal Kenji. Mechanisms of attention in visual cortex and the amygdala. [New York, N.Y.?]: [publisher not identified], 2016.

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1939-, Pfaff Donald W., and Kieffer Brigitte L, eds. Molecular and biophysical mechanisms of arousal, alertness, and attention. Boston, Mass: Published by Blackwell Pub. on behalf of the New York Academy of Sciences, 2008.

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Частини книг з теми "Mechanism of attention"

al-Rifaie, Mohammad Majid, and John Mark Bishop. "Swarmic Sketches and Attention Mechanism." In Evolutionary and Biologically Inspired Music, Sound, Art and Design, 85–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36955-1_8.

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Ahuja, Stuti, Aftaabahmed Sheikh, Shubhadarshini Nadar, and Vanitha Shunmugaperumal. "Video Descriptor Using Attention Mechanism." In Communications in Computer and Information Science, 168–78. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12638-3_15.

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Dong, Hongbin, Lei Yang, and Kunming Han. "Collaborative Filtering Based on Attention Mechanism." In Communications in Computer and Information Science, 3–14. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9298-7_1.

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Kakanakou, Miguel, Hongwei Xie, and Yan Qiang. "Double Attention Mechanism for Sentence Embedding." In Web Information Systems and Applications, 228–39. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02934-0_21.

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Nguyen, Bao T., Om Prakash, and Anh H. Vo. "Attention Mechanism for Fashion Image Captioning." In Advances in Intelligent Systems and Computing, 93–104. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62324-1_9.

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Wu, Qian, Chunjie Cao, Jianbin Mai, and Fangjian Tao. "Robust GAN Based on Attention Mechanism." In Cyberspace Safety and Security, 78–86. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73671-2_8.

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Velpuri, Sai Yashwanth, Sonakshi Karanwal, and R. Anita. "Neural Machine Translation Using Attention Mechanism." In Proceedings of International Conference on Recent Trends in Computing, 717–29. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7118-0_61.

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Kaman, Sweta. "Attention Mechanism-Based News Sentiment Analyzer." In Innovations in Computer Science and Engineering, 235–40. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4543-0_25.

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Loureiro, Cátia, Vítor Filipe, and Lio Gonçalves. "Attention Mechanism for Classification of Melanomas." In Communications in Computer and Information Science, 65–77. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-23236-7_5.

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Azad, Reza, Maryam Asadi-Aghbolaghi, Mahmood Fathy, and Sergio Escalera. "Attention Deeplabv3+: Multi-level Context Attention Mechanism for Skin Lesion Segmentation." In Computer Vision – ECCV 2020 Workshops, 251–66. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66415-2_16.

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Тези доповідей конференцій з теми "Mechanism of attention"

Zheyuan, Wang, Mao Yingchi, Shuai Zhang, Yong Qian, Zhang Zeyu, and Chen Zhihao. "Scale Attention Mechanism." In 2022 IEEE Eighth International Conference on Big Data Computing Service and Applications (BigDataService). IEEE, 2022. http://dx.doi.org/10.1109/bigdataservice55688.2022.00031.

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Shanthamallu, Uday Shankar, Jayaraman J. Thiagarajan, and Andreas Spanias. "A Regularized Attention Mechanism for Graph Attention Networks." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9054363.

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Atiwetsakun, Jednipat, and Santitham Prom-on. "Thai Tokenization with Attention Mechanism." In 2021 2nd International Conference on Big Data Analytics and Practices (IBDAP). IEEE, 2021. http://dx.doi.org/10.1109/ibdap52511.2021.9552074.

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Wu, Zhuanghui, Guoheng Huang, and Lianglun Cheng. "An Effective Visual Attention Mechanism." In Proceedings of the 2019 International Conference on Computer, Network, Communication and Information Systems (CNCI 2019). Paris, France: Atlantis Press, 2019. http://dx.doi.org/10.2991/cnci-19.2019.47.

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Liu, Zhipeng, Wei Fang, and Jun Sun. "An effective lightweight attention mechanism." In 2021 20th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES). IEEE, 2021. http://dx.doi.org/10.1109/dcabes52998.2021.00042.

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Liu, Ying, Wei Wang, Tianlin Zhang, and Zhenyu Cui. "AttentionFM: Incorporating Attention Mechanism and Factorization Machine for Credit Scoring." In 2020 International Conference on Data Mining Workshops (ICDMW). IEEE, 2020. http://dx.doi.org/10.1109/icdmw51313.2020.00056.

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Fukui, Hiroshi, Tsubasa Hirakawa, Takayoshi Yamashita, and Hironobu Fujiyoshi. "Attention Branch Network: Learning of Attention Mechanism for Visual Explanation." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.01096.

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Huang, Zhongjie, Songlin Sun, and Yuhao Liu. "Person Search Based on Attention Mechanism." In 2019 19th International Symposium on Communications and Information Technologies (ISCIT). IEEE, 2019. http://dx.doi.org/10.1109/iscit.2019.8905176.

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Khandelwal, Siddhesh, and Leonid Sigal. "AttentionRNN: A Structured Spatial Attention Mechanism." In 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2019. http://dx.doi.org/10.1109/iccv.2019.00352.

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Yang, Hua. "Extended Attention Mechanism for TSP Problem." In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9533472.

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Звіти організацій з теми "Mechanism of attention"

Olton, David S., Kevin Pang, and Howard Egeth. Neural Mechanisms of Attention. Fort Belvoir, VA: Defense Technical Information Center, May 1993. http://dx.doi.org/10.21236/ada266315.

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Olton, David, Howard Egeth, and Kevin Pang. Neural Mechanisms of Attention. Fort Belvoir, VA: Defense Technical Information Center, November 1989. http://dx.doi.org/10.21236/ada216478.

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Shulman, Gordon L. Relating Attention to Visual Mechanisms. Fort Belvoir, VA: Defense Technical Information Center, February 1989. http://dx.doi.org/10.21236/ada206452.

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Woldorff, M. G. Brain Attention Mechanisms in Perception and Performance. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada422630.

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Chapin, John K. Cortical Mechanisms of Attention, Discrimination, and Motor Response to Somaesthetic Stimuli. Fort Belvoir, VA: Defense Technical Information Center, December 1991. http://dx.doi.org/10.21236/ada247228.

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Synchak, Bohdan. Freedom of choice and freedom of action in the Ukrainian media. Ivan Franko National University of Lviv, February 2022. http://dx.doi.org/10.30970/vjo.2022.51.11400.

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The article talks about the philosophical foundations that characterize the mechanism of internal inducement to action. As an academic, constitutional, and socio-ideological concept, the boundaries of freedom are outlined, which are displayed in the field of modern media space. The term «freedom» is considered as several philosophical concepts that formed the basis of the modern interpretation of this concept. The totality of its meanings is generalized into one that is adapted for the modern system. Parallels are drawn between the interaction of the concept of user freedom with the plane of domestic mass media because despite, the fact that consciousness is knowledge, the incoming information directly affects the individual and collective consciousness. Using the example of the most popular digital platforms, the components of the impact on users and the legal aspect of their implementation are analyzed. When considering the issues of freedom of choice and freedom of action on the Internet, special attention is paid to methods of collecting and processing information, in particular, the limitations and possibilities of digital programs-algorithms of the popular search engine Google. The types of personal information collected by Google about the user are classified and the possible mechanisms of influence on personal choice and access to information on the Internet are characterized. The article analyzes the constitutional guarantees of freedom and the impact of digital technologies on them. Particular attention is paid to ethics, in particular journalistic, which nominally regulates the limits of the humane, permissible, a / moral (unacceptable/acceptable) in the implementation of professional information activities in the media. Thus, the issue of freedom of choice and freedom of action in the plane of domestic mass media is subject to an objective examination of its components, they are analyzed for a proper constitutionally suitable phenomenon, which must be investigated from the point of view of compliance with human rights and freedoms and professional standards within the media.

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Shulman, Gordon L., and Michael I. Posner. Relating Sensitivity and Criterion Effects to the Internal Mechanisms of Visual Spatial Attention. Fort Belvoir, VA: Defense Technical Information Center, April 1988. http://dx.doi.org/10.21236/ada197088.

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Cooper, Rachel, and Roz Price. Water Security and Climate Change. Institute of Development Studies (IDS), July 2021. http://dx.doi.org/10.19088/k4d.2021.116.

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Attention is coalescing around water and climate change, and the agendas for water security and climate action are converging. There is growing appreciation of water as a crosscutting mechanism for improving the effectiveness of global and national climate change policies (Smith et al., 2019). Water has long been recognised as a central component of climate change impacts as well as being an important consideration in mitigation and effective adaptation – where it can be both an enabling factor and a limiting factor (UN-Water, 2019). However, the connections go beyond just recognising the importance of water for climate change or simply making the “water sector” climate resilient; there is a need for system-wide coherence on water across different national and international agendas and transformational change of water management (UNESCO, UN-Water, 2020). The water crisis and the climate crisis require urgent action, and call for sustained and integrated support, leveraging complementary resources, with enhanced coordination in the context of growing uncertainties. As countries review and implement their Nationally Determined Contributions (NDCs) as part of the Paris Agreement, there is a unique opportunity to improve and enhance water management practices to increase climate resilience, improve ecosystems and reduce the risk of water-related disasters (UN-Water, 2019).

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Naess, Lars Otto, Jan Selby, and Gabrielle Daoust. Climate Resilience and Social Assistance in Fragile and Conflict-Affected Settings. Institute of Development Studies (IDS), February 2022. http://dx.doi.org/10.19088/basic.2022.002.

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This paper aims to improve our understanding of the nature, causes, and multiple dimensions of how social assistance may address climate vulnerability and resilience within fragile and conflict-affected settings (FCAS), as part of the inception phase of the Better Assistance in Crises (BASIC) Research programme. Over recent years, social assistance, such as cash transfers and voucher programmes, has been seen as a way of reducing the impacts of climate-related shocks and stressors, and of increasing the resilience of recipient households and communities. It has also been seen as a mechanism for delivering adaptation funding, showing promise in tackling short-term shocks as well as longer-term adaptation to climate change. Yet despite FCAS hosting some of the most vulnerable populations in the world, so far there has been little attention to these settings. We examine the linkages between social assistance and climate resilience in FCAS and in turn, implications for BASIC Research. Specifically, we ask what the evidence is on whether existing approaches to social assistance are appropriate to reducing climate vulnerabilities and building climate resilience in FCAS, and, if not, how they might be reformed. We address this through three sub-questions. First, what are the major conceptual discussions on climate resilience and social assistance, and what is the extent of work in FCAS? This is addressed in section 2.1, based on an extensive literature review. Second, to what extent does the literature on social assistance and climate resilience apply to the particular concerns of FCAS? This is covered in section 2.2, based on a framework informed by work in political economy and political ecology. Third, what are possible future research directions? We conclude with reflections on what BASIC Research may contribute in section 3.

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Chefetz, Benny, Baoshan Xing, Leor Eshed-Williams, Tamara Polubesova, and Jason Unrine. DOM affected behavior of manufactured nanoparticles in soil-plant system. United States Department of Agriculture, January 2016. http://dx.doi.org/10.32747/2016.7604286.bard.

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The overall goal of this project was to elucidate the role of dissolved organic matter (DOM) in soil retention, bioavailability and plant uptake of silver and cerium oxide NPs. The environmental risks of manufactured nanoparticles (NPs) are attracting increasing attention from both industrial and scientific communities. These NPs have shown to be taken-up, translocated and bio- accumulated in plant edible parts. However, very little is known about the behavior of NPs in soil-plant system as affected by dissolved organic matter (DOM). Thus DOM effect on NPs behavior is critical to assessing the environmental fate and risks related to NP exposure. Carbon-based nanomaterials embedded with metal NPs demonstrate a great potential to serve as catalyst and disinfectors. Hence, synthesis of novel carbon-based nanocomposites and testing them in the environmentally relevant conditions (particularly in the DOM presence) is important for their implementation in water purification. Sorption of DOM on Ag-Ag₂S NPs, CeO₂ NPs and synthesized Ag-Fe₃O₄-carbon nanotubebifunctional composite has been studied. High DOM concentration (50mg/L) decreased the adsorptive and catalytic efficiencies of all synthesized NPs. Recyclable Ag-Fe₃O₄-carbon nanotube composite exhibited excellent catalytic and anti-bacterial action, providing complete reduction of common pollutants and inactivating gram-negative and gram-positive bacteria at environmentally relevant DOM concentrations (5-10 mg/L). Our composite material may be suitable for water purification ranging from natural to the industrial waste effluents. We also examined the role of maize (Zeamays L.)-derived root exudates (a form of DOM) and their components on the aggregation and dissolution of CuONPs in the rhizosphere. Root exudates (RE) significantly inhibited the aggregation of CuONPs regardless of ionic strength and electrolyte type. With RE, the critical coagulation concentration of CuONPs in NaCl shifted from 30 to 125 mM and the value in CaCl₂ shifted from 4 to 20 mM. This inhibition was correlated with molecular weight (MW) of RE fractions. Higher MW fraction (> 10 kDa) reduced the aggregation most. RE also significantly promoted the dissolution of CuONPs and lower MW fraction (< 3 kDa) RE mainly contributed to this process. Also, Cu accumulation in plant root tissues was significantly enhanced by RE. This study provides useful insights into the interactions between RE and CuONPs, which is of significance for the safe use of CuONPs-based antimicrobial products in agricultural production. Wheat root exudates (RE) had high reducing ability to convert Ag+ to nAg under light exposure. Photo-induced reduction of Ag+ to nAg in pristine RE was mainly attributed to the 0-3 kDa fraction. Quantification of the silver species change over time suggested that Cl⁻ played an important role in photoconversion of Ag+ to nAg through the formation and redox cycling of photoreactiveAgCl. Potential electron donors for the photoreduction of Ag+ were identified to be reducing sugars and organic acids of low MW. Meanwhile, the stabilization of the formed particles was controlled by both low (0-3 kDa) and high (>3 kDa) MW molecules. This work provides new information for the formation mechanism of metal nanoparticles mediated by RE, which may further our understanding of the biogeochemical cycling and toxicity of heavy metal ions in agricultural and environmental systems. Copper sulfide nanoparticles (CuSNPs) at 1:1 and 1:4 ratios of Cu and S were synthesized, and their respective antifungal efficacy was evaluated against the pathogenic activity of Gibberellafujikuroi(Bakanae disease) in rice (Oryza sativa). In a 2-d in vitro study, CuS decreased G. fujikuroiColony- Forming Units (CFU) compared to controls. In a greenhouse study, treating with CuSNPs at 50 mg/L at the seed stage significantly decreased disease incidence on rice while the commercial Cu-based pesticide Kocide 3000 had no impact on disease. Foliar-applied CuONPs and CuS (1:1) NPs decreased disease incidence by 30.0 and 32.5%, respectively, which outperformed CuS (1:4) NPs (15%) and Kocide 3000 (12.5%). CuS (1:4) NPs also modulated the shoot salicylic acid (SA) and Jasmonic acid (JA) production to enhance the plant defense mechanisms against G. fujikuroiinfection. These results are useful for improving the delivery efficiency of agrichemicals via nano-enabled strategies while minimizing their environmental impact, and advance our understanding of the defense mechanisms triggered by the NPs presence in plants.

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