Relevant bibliographies by topics / Frontière neurale

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Frontière neurale'

Author: Grafiati
Published: 18 May 2024

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Journal articles on the topic "Frontière neurale"

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Xu Mingliang, 徐明亮, 李芳媛 Li Fangyuan, 刘岳圻 Liu Yueqi, 张瑾慧 Zhang Jinhui, 师亚洲 Shi Yazhou, and 何飞 He Fei. "植入式多模态神经接口前沿进展." Chinese Journal of Lasers 50, no. 15 (2023): 1507301. http://dx.doi.org/10.3788/cjl221482.

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Rey, Federica, Bianca Barzaghini, Alessandra Nardini, Matteo Bordoni, Gian Vincenzo Zuccotti, Cristina Cereda, Manuela Teresa Raimondi, and Stephana Carelli. "Advances in Tissue Engineering and Innovative Fabrication Techniques for 3-D-Structures: Translational Applications in Neurodegenerative Diseases." Cells 9, no. 7 (July 7, 2020): 1636. http://dx.doi.org/10.3390/cells9071636.

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In the field of regenerative medicine applied to neurodegenerative diseases, one of the most important challenges is the obtainment of innovative scaffolds aimed at improving the development of new frontiers in stem-cell therapy. In recent years, additive manufacturing techniques have gained more and more relevance proving the great potential of the fabrication of precision 3-D scaffolds. In this review, recent advances in additive manufacturing techniques are presented and discussed, with an overview on stimulus-triggered approaches, such as 3-D Printing and laser-based techniques, and deposition-based approaches. Innovative 3-D bioprinting techniques, which allow the production of cell/molecule-laden scaffolds, are becoming a promising frontier in disease modelling and therapy. In this context, the specific biomaterial, stiffness, precise geometrical patterns, and structural properties are to be considered of great relevance for their subsequent translational applications. Moreover, this work reports numerous recent advances in neural diseases modelling and specifically focuses on pre-clinical and clinical translation for scaffolding technology in multiple neurodegenerative diseases.
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Liu, Feng, Jianjun Pang, and Zhiwei Xu. "Multi-Objective Optimization of Injection Molding Process Parameters for Moderately Thick Plane Lens Based on PSO-BPNN, OMOPSO, and TOPSIS." Processes 12, no. 1 (December 22, 2023): 36. http://dx.doi.org/10.3390/pr12010036.

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Injection molding (IM) is an ideal technique for the low-cost mass production of moderately thick plane lenses (MTPLs). However, the optical performance of injection molded MTPL is seriously degraded by the warpage and sink marks induced during the molding process with complex historical thermal field changes. Thus, it is essential that the processing parameters utilized in the molding process are properly assigned. And the challenges are further compounded when considering the MTPL molding energy consumption. This paper presents a set of procedures for the optimization of injection molding process parameters, with warpage, sink marks reflecting the optical performance, and clamping force reflecting the molding energy consumption as the optimization objectives. First, the orthogonal experiment was carried out with the Taguchi method, and the S/N response shows that these three objectives cannot reach the optimal values simultaneously. Second, considering the experimental data scale, the back propagation neural network updated by the particle swarm optimization method (PSO-BPNN) was applied to establish the complex nonlinear mapping relationship between the process parameters and these three trade-off objectives respectively. Then, the Pareto optimal frontier of the multi-objective optimization problem was attained by multi-objective particle swarm optimization using a mutation operator and dominance coefficient algorithm (OMOPSO). And the competitive relationship between these objectives was further confirmed by the corresponding pairwise Pareto frontiers. Additionally, the TOPSIS method with equal weights was employed to achieve the best optimal solution from the Pareto optimal frontier. The simulation results yielded that the maximum values of warpage, sink marks, and clamping force could be reduced by 7.44%, 40.56%, and 5.56%, respectively, after optimization. Finally, MTPL products were successfully fabricated.
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Justen, Lennart, Duncan Carlsmith, Susan M. Paskewitz, Lyric C. Bartholomay, and Gebbiena M. Bron. "Identification of public submitted tick images: A neural network approach." PLOS ONE 16, no. 12 (December 2, 2021): e0260622. http://dx.doi.org/10.1371/journal.pone.0260622.

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Ticks and tick-borne diseases represent a growing public health threat in North America and Europe. The number of ticks, their geographical distribution, and the incidence of tick-borne diseases, like Lyme disease, are all on the rise. Accurate, real-time tick-image identification through a smartphone app or similar platform could help mitigate this threat by informing users of the risks associated with encountered ticks and by providing researchers and public health agencies with additional data on tick activity and geographic range. Here we outline the requirements for such a system, present a model that meets those requirements, and discuss remaining challenges and frontiers in automated tick identification. We compiled a user-generated dataset of more than 12,000 images of the three most common tick species found on humans in the U.S.: Amblyomma americanum, Dermacentor variabilis, and Ixodes scapularis. We used image augmentation to further increase the size of our dataset to more than 90,000 images. Here we report the development and validation of a convolutional neural network which we call “TickIDNet,” that scores an 87.8% identification accuracy across all three species, outperforming the accuracy of identifications done by a member of the general public or healthcare professionals. However, the model fails to match the performance of experts with formal entomological training. We find that image quality, particularly the size of the tick in the image (measured in pixels), plays a significant role in the network’s ability to correctly identify an image: images where the tick is small are less likely to be correctly identified because of the small object detection problem in deep learning. TickIDNet’s performance can be increased by using confidence thresholds to introduce an “unsure” class and building image submission pipelines that encourage better quality photos. Our findings suggest that deep learning represents a promising frontier for tick identification that should be further explored and deployed as part of the toolkit for addressing the public health consequences of tick-borne diseases.
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Le Merrer, Erwan, Patrick Pérez, and Gilles Trédan. "Adversarial frontier stitching for remote neural network watermarking." Neural Computing and Applications 32, no. 13 (August 17, 2019): 9233–44. http://dx.doi.org/10.1007/s00521-019-04434-z.

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Tsionas, Mike, Christopher F. Parmeter, and Valentin Zelenyuk. "Bayesian Artificial Neural Networks for frontier efficiency analysis." Journal of Econometrics 236, no. 2 (October 2023): 105491. http://dx.doi.org/10.1016/j.jeconom.2023.105491.

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Jin, Ding. "Portfolio Management Based on TMT Sector: Comparative Study between Basic Qualitative and Model-Based Approach." BCP Business & Management 38 (March 2, 2023): 579–91. http://dx.doi.org/10.54691/bcpbm.v38i.3742.

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Nowadays, more and more investment techniques are incorporating model-based techniques to facilitate portfolio management process; however, techniques that could predict future stock expected returns are relatively scarce. This study mainly focuses on using model-based methods to evaluate stocks in the TMT (Technology, Media, Telecom) sector based on historical data for the last four years. LSTM (Long Short-Term Memory) neural network and Fama-French three factor models are employed in this study to predict the future expected return of the stocks and further, evaluate whether the stock could come into the optimal portfolio. Efficient frontiers are drawn using the variance of expected return against the mean of future expected return. Then investment utility function is set up along with the efficient frontier to make optimizer to get the best weight of the optimal portfolio. To justify if the model-based approach is robust, comparative study is done between basic qualitative approach and model-based approach. Two parallel approaches will use different methods as well as metrics to evaluate the same set of competitor stocks and generate the optimal portfolio. The results have shown that both approaches have the same optimal portfolio and the model-based approach is justified. Thus, this quantitative model-based approach is robust and applicable for investment since it could generate consistent result as the basic qualitative approach and it is more explicit in data.
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Akter, Mst Shapna, Hossain Shahriar, Reaz Chowdhury, and M. R. C. Mahdy. "Forecasting the Risk Factor of Frontier Markets: A Novel Stacking Ensemble of Neural Network Approach." Future Internet 14, no. 9 (August 25, 2022): 252. http://dx.doi.org/10.3390/fi14090252.

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Forecasting the risk factor of the financial frontier markets has always been a very challenging task. Unlike an emerging market, a frontier market has a missing parameter named “volatility”, which indicates the market’s risk and as a result of the absence of this missing parameter and the lack of proper prediction, it has almost become difficult for direct customers to invest money in frontier markets. However, the noises, seasonality, random spikes and trends of the time-series datasets make it even more complicated to predict stock prices with high accuracy. In this work, we have developed a novel stacking ensemble of the neural network model that performs best on multiple data patterns. We have compared our model’s performance with the performance results obtained by using some traditional machine learning ensemble models such as Random Forest, AdaBoost, Gradient Boosting Machine and Stacking Ensemble, along with some traditional deep learning models such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term (BiLSTM). We have calculated the missing parameter named “volatility” using stock price (Close price) for 20 different companies of the frontier market and then made predictions using the aforementioned machine learning ensemble models, deep learning models and our proposed stacking ensemble of the neural network model. The statistical evaluation metrics RMSE and MAE have been used to evaluate the performance of the models. It has been found that our proposed stacking ensemble neural network model outperforms all other traditional machine learning and deep learning models which have been used for comparison in this paper. The lowest RMSE and MAE values we have received using our proposed model are 0.3626 and 0.3682 percent, respectively, and the highest RMSE and MAE values are 2.5696 and 2.444 percent, respectively. The traditional ensemble learning models give the highest RMSE and MAE error rate of 20.4852 and 20.4260 percent, while the deep learning models give 15.2332 and 15.1668 percent, respectively, which clearly states that our proposed model provides a very low error value compared with the traditional models.
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Furber, Steve, and Steve Temple. "Neural systems engineering." Journal of The Royal Society Interface 4, no. 13 (November 28, 2006): 193–206. http://dx.doi.org/10.1098/rsif.2006.0177.

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The quest to build an electronic computer based on the operational principles of biological brains has attracted attention over many years. The hope is that, by emulating the brain, it will be possible to capture some of its capabilities and thereby bridge the very large gulf that separates mankind from machines. At present, however, knowledge about the operational principles of the brain is far from complete, so attempts at emulation must employ a great deal of assumption and guesswork to fill the gaps in the experimental evidence. The sheer scale and complexity of the human brain still defies attempts to model it in its entirety at the neuronal level, but Moore's Law is closing this gap and machines with the potential to emulate the brain (so far as we can estimate the computing power required) are no more than a decade or so away. Do computer engineers have something to contribute, alongside neuroscientists, psychologists, mathematicians and others, to the understanding of brain and mind, which remains as one of the great frontiers of science?
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Cohen, Avis H. "Neural Regeneration and Transplantation (Frontiers of Clinical Neuroscience Vol. 6)." Trends in Neurosciences 13, no. 2 (February 1990): 77–78. http://dx.doi.org/10.1016/0166-2236(90)90074-k.

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Dissertations / Theses on the topic "Frontière neurale"

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LANDI, FEDERICO. "Percepire, Ragionare, Agire: la Nuova Frontiera dell’Embodied AI." Doctoral thesis, Università degli studi di Modena e Reggio Emilia, 2022. http://hdl.handle.net/11380/1271185.

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Questa tesi contribuisce al campo dell'Intelligenza Artificiale Incorporata (Embodied AI). L'Embodied AI è un nuovo argomento di ricerca all'intersezione tra visione artificiale e robotica e sfrutta le recenti scoperte sulle reti neurali. Il nostro obiettivo è quello di creare agenti intelligenti in grado di: percepire il mondo, ragionare sulle relazioni spazio-temporali e agire per raggiungere un obiettivo predefinito. Per affrontare questo problema, identifichiamo una strategia adeguata a questo argomento complesso, che comporta la gestione da un lato di serie temporali e dipendenze a lungo termine, e dall’altro di input provenienti da diversi domini. Distinguiamo tre diversi problemi che dobbiamo affrontare per costruire un agente intelligente. Partiamo dal problema delle dipendenze a lungo termine e della modellazione della sequenza, in quanto l'agente ha bisogno di elaborare i dati provenienti da una serie di passaggi temporali che fungono da esperienza precedente. Quindi, consideriamo e affrontiamo una prima semplice forma di interazione con un ambiente sconosciuto: l'esplorazione. In questo modo, combiniamo il ragionamento visivo e spaziale eseguire semplici azioni. Infine, studiamo come incorporare istruzioni in linguaggio naturale per guidare la navigazione dell'agente verso un obiettivo. Il linguaggio diventa quindi un'interfaccia naturale per comunicare con l'agente, aprendo le porte a ricerche e applicazioni future. In questa tesi presentiamo un'analisi di queste caratteristiche che ogni agente intelligente dovrebbe possedere. Nel fare ciò, proponiamo una panoramica completa del campo dell’Embodied AI, i suoi fondamenti teorici, i dataset e i benchmark stato dell’arte e alcune indicazioni pratiche relative all'implementazione dell'agente risultante nel mondo reale. Nella prima parte di questa tesi, vengono discusse le Reti Neurali Ricorrenti (RNN), la tecnologia più comune per modellare serie temporali, e in particolare la Long Short-Term Memory (LSTM): lo standard di fatto per molti problemi che coinvolgono input sequenziali e dipendenze a lungo termine. In quanto tali, rappresentano una tecnologia abilitante per l'Embodied AI. Introduciamo un miglioramento euristico nella LSTM che porta a risultati migliori, maggiore stabilità durante il training e tempi di convergenza ridotti su una serie di problemi. A seguire, collochiamo l'agente in un ambiente fotorealistico simulato. Il nostro obiettivo è esplorare più area possibile in questo nuovo ambiente in un intervallo di tempo prefissato. A tal fine, proponiamo due diverse configurazioni di training: un primo approccio basato sulla curiosità, in cui l'agente cerca di massimizzare la sua sorpresa durante l'episodio di esplorazione, e una seconda strategia basata sull'impatto dell'azione dell'agente sull'ambiente. Mostriamo che l'esplorazione è un'abilità essenziale per un agente e che può abilitare una serie di capacità più specializzate come descrivere una scena o navigare verso coordinate relative in ambienti sconosciuti. Quindi affrontiamo il recente compito della navigazione visuale guidata da linguaggio (VLN). In questo caso, l'agente deve seguire un'istruzione testuale per raggiungere la sua destinazione in un ambiente completamente nuovo. Con questo in mente, proponiamo due diversi metodi per fondere le informazioni linguali e visive: uno basato su filtri convolutivi dinamici e l'altro basato su attenzione. In questo modo, mostriamo che è possibile includere istruzioni in linguaggio naturale provenienti da un utente umano nel motore di ragionamento dell'agente. Questa possibilità apre poi le porte una serie di future direzioni di ricerca e applicazioni. Come contributo finale, discutiamo come portare agenti addestrati alla simulazione nel mondo reale. Mentre la maggior parte dei nostri esperimenti sfrutta la simulazione, mostriamo che è possibile utilizzare i modelli risultanti su un Low-Cost Robot (LoCoBot) con pochi accorgimenti.
This thesis contributes to the field of Embodied Artificial Intelligence. Embodied AI is a novel research topic at the intersection of Computer Vision and Robotics and takes advantage of recent findings on Deep Neural Networks. Empowered by the so-called "deep revolution", we strive to create intelligent agents able to: perceive the world, reason about Spatio-temporal relationships, and act to reach a pre-defined goal. First, we need to identify a proper strategy to tackle such a complex topic, which entails time series and long-term dependencies on one end and multiple input modalities on the other end. We distinguish three different problems we need to address to build an intelligent agent. We start from the problem of long-term dependencies and sequence modeling, as the agent needs to process data coming from a sequence of time steps acting as previous experience. Then, we consider and tackle a first simple form of interaction with an unknown environment: exploration. In this way, we combine visual and spatial reasoning to perform simple actions such as in-place rotations and moving forward. Finally, we study how to incorporate natural language instructions to guide the agent's navigation towards a goal. Language then becomes a natural interface to communicate with the agent, paving the way to future research and applications. This thesis presents a step-by-step analysis of these features that any intelligent agent should possess. While doing so, we cover a comprehensive overview of the field, theoretical foundations for Embodied AI, state-of-the-art datasets and benchmarks, and practical indications regarding the deployment of the resulting agent in the real world. In the first part of this thesis, we discuss Recurrent Neural Networks (RNNs). RNNs are the most common approach when dealing with time series. IN particular, Long Short-Term Memory (LSTM) is the standard de-facto for many tasks involving sequential inputs and long-term dependencies. As such, they represent an enabling technology for Embodied AI. We introduce a heuristic enhancement of LSTM that brings better results, increased training stability, and reduced convergence time on a set of tasks. In the following, we place the agent in a simulated photorealistic unknown environment. We aim to explore the largest portion of the environment new scene in a fixed amount of time. To that end, we propose two different training setups. The first approach relies on curiosity, where the agent tries to maximize its surprisal during the exploration episode. The second strategy promotes actions likely to produce a high impact (i.e., visual changes) on the environment. We show that exploration is an essential ability of embodied agents and that it can enable a series of downstream tasks such as scene description and coordinate-driven navigation in unknown environments. Then we tackle the recent task of Vision-and-Language Navigation (VLN). In VLN, the agent needs to follow a language-specified instruction to reach a target location in a new environment. With that in mind, we propose two different methods to fuse lingual and visual information: one based on dynamic convolutional filters and the other based on attention. This way, we show that it is possible to include natural language instructions from a human user in the agent reasoning motor. Hence, we enable a series of future research directions and applications. As a final contribution, we discuss how to deploy agents trained in simulation in the real world. While most of our experiments exploit simulation, we show that it is possible to deploy the resulting models on a Low-Cost Robot (LoCoBot) with little effort.
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Egilmez, Gokhan. "Road Safety Assessment of U.S. States: A Joint Frontier and Neural Network ModelingApproach." Ohio University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1374854708.

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Bally-Cuif, Laure. "Developpement de la région met-mésencéphalique du tube neural : rôle du gène Wnt-1." Paris 6, 1994. http://www.theses.fr/1994PA066809.

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Giavelli, Francesca. "DeepL: la nuova frontiera della traduzione automatica neurale a confronto con il linguaggio enologico. Uno studio basato sulla traduzione del sito della Cantina di Cesena." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/15177/.

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Quando a Novembre 2017 ho cominciato a pensare alla mia idea di tesi, ho deciso di voler portare a compimento il mio lavoro di traduzione del sito della Cantina di Cesena. La mia relatrice, la prof.ssa Heiss, mi ha poi consigliato di lavorare e confrontare le mie traduzioni, vecchie e nuove, con il nuovo traduttore automatico DeepL: si riteneva interessante rilevare quali risultati questo avrebbe potuto produrre quando messo a confronto con diversi tipi di testi e con un linguaggio assai specifico, come quello enologico. Il lavoro che ne è risultato è assai ampio e variegato, e questa tesi ne riporta solo una parte, quella dell’analisi di DeepL a confronto con questo tipo di traduzione; il resto si può trovare in Appendice e, parzialmente, on-line all’indirizzo: www.cantinacesena.it. La traduzione delle schede dei vini ha portato alla creazione di un breve glossario bilingue due parti nel quale si sono raccolti i termini specifici utilizzati: una parte riguarda la terminologia enologica, l’altra, riguarda la terminologia gastronomica utilizzata nelle schede dei vini sotto la voce “Abbinamenti:”, in cui vengono suggeriti i piatti tipici da accostare ad ogni vino. A fine lavoro, tutti i testi sono stati caricati sul sito, grazie al permesso concessomi dai dirigenti della Cantina di Cesena di lavorarci; l’upload on-line ha avuto delle limitazioni, dovute alle credenziali fornitemi, che permettono solamente la modifica dei contenuti, non della struttura: in alcune pagine è possibile individuare errori e parti non tradotte. Di tali errori si dà una breve illustrazione in Appendice, dove sono inserite alcune pagine (Screenshot) salvate dal sito web.
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Savan, Emanuel-Emil. "Consumer liking and sensory attribute prediction for new product development support : applications and enhancements of belief rule-based methodology." Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/consumer-liking-and-sensory-attribute-prediction-for-new-product-development-support-applications-and-enhancements-of-belief-rulebased-methodology(0582be52-a5ce-47da-836d-e30b5506fb41).html.

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Methodologies designed to support new product development are receiving increasing interest in recent literature. A significant percentage of new product failure is attributed to a mismatch between designed product features and consumer liking. A variety of methodologies have been proposed and tested for consumer liking or preference prediction, ranging from statistical methodologies e.g. multiple linear regression (MLR) to non-statistical approaches e.g. artificial neural networks (ANN), support vector machines (SVM), and belief rule-based (BRB) systems. BRB has been previously tested for consumer preference prediction and target setting in case studies from the beverages industry. Results have indicated a number of technical and conceptual advantages which BRB holds over the aforementioned alternative approaches. This thesis focuses on presenting further advantages and applications of the BRB methodology for consumer liking prediction. The features and advantages are selected in response to challenges raised by three addressed case studies. The first case study addresses a novel industry for BRB application: the fast moving consumer goods industry, the personal care sector. A series of challenges are tackled. Firstly, stepwise linear regression, principal component analysis and AutoEncoder are tested for predictors’ selection and data reduction. Secondly, an investigation is carried out to analyse the impact of employing complete distributions, instead of averages, for sensory attributes. Moreover, the effect of modelling instrumental measurement error is assessed. The second case study addresses a different product from the personal care sector. A bi-objective prescriptive approach for BRB model structure selection and validation is proposed and tested. Genetic Algorithms and Simulated Annealing are benchmarked against complete enumeration for searching the model structures. A novel criterion based on an adjusted Akaike Information Criterion is designed for identifying the optimal model structure from the Pareto frontier based on two objectives: model complexity and model fit. The third case study introduces yet another novel industry for BRB application: the pastry and confectionary specialties industry. A new prescriptive framework, for rule validation and random training set allocation, is designed and tested. In all case studies, the BRB methodology is compared with the most popular alternative approaches: MLR, ANN, and SVM. The results indicate that BRB outperforms these methodologies both conceptually and in terms of prediction accuracy.
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Wu, Hsin-Yun, and 吳欣芸. "CURRENCY BASKET HEDGE — THE COMPARISON BETWEEN EFFICENT FRONTIER AND ARTIFICIAL NEURAL NETWORK." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/40459188698317435140.

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碩士
元智大學
管理研究所
96
Life insurance companies in Taiwan enjoy better returns for their investment abroad. These investment in foreign currency are therefore subject to exchange risk and substantial FX volatility when insurances move their massive currency position from one to another, thus it is required by the Central Bank that these overseas positions are 100% FX-hedged. Another main cause for fluctuating returns is the swinging hedging cost by which total return for foreign investment in late 2006 was effectively overtaken by domestic one. Currency Basket Hedge was then developed for FX-risk-aversion and the pursuit of lower hedging cost. Based on the model of Constant Tracking Error, this research aims to identify, by using two models constructed, the optimal weighting of each composite in a currency basket. Results from respective models are then compared for effectiveness in hedging and cost level. The data sources include historical daily closing price and forward closing price within a given time frame (by using the Back-Propagation Network to forecast). The former assumes the historical price movement is identical to the one for the future, whereas the latter assumes what is forecast by the Back-Propagation Network is the same vis-à-vis the future price movement. Also, this research uses NNTool within the Matlab Package, as it is more suitable for users who are not familiar with computer programming, which will also enable the concepts of Back-Propagation Network to better comprehended. This research shows that both models serve the objectives reasonably well, yet they have different advantages in the two objectives and portfolio managers can choose accordingly for the set-up of currency basket hedging.
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raganato, alessandro. "New frontiers in supervised word sense disambiguation: building multilingual resources and neural models on a large scale." Doctoral thesis, 2018. http://hdl.handle.net/11573/1066474.

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Word Sense Disambiguation is a long-standing task in Natural Language Processing (NLP), lying at the core of human language understanding. While it has already been studied from many different angles over the years, ranging from knowledge based systems to semi-supervised and fully supervised models, the field seems to be slowing down in respect to other NLP tasks, e.g., part-of-speech tagging and dependencies parsing. Despite the organization of several international competitions aimed at evaluating Word Sense Disambiguation systems, the evaluation of automatic systems has been problematic mainly due to the lack of a reliable evaluation framework aiming at performing a direct quantitative confrontation. To this end we develop a unified evaluation framework and analyze the performance of various Word Sense Disambiguation systems in a fair setup. The results show that supervised systems clearly outperform knowledge-based models. Among the supervised systems, a linear classifier trained on conventional local features still proves to be a hard baseline to beat. Nonetheless, recent approaches exploiting neural networks on unlabeled corpora achieve promising results, surpassing this hard baseline in most test sets. Even though supervised systems tend to perform best in terms of accuracy, they often lose ground to more flexible knowledge-based solutions, which do not require training for every disambiguation target. To bridge this gap we adopt a different perspective and rely on sequence learning to frame the disambiguation problem: we propose and study in depth a series of end-to-end neural architectures directly tailored to the task, from bidirectional Long ShortTerm Memory to encoder-decoder models. Our extensive evaluation over standard benchmarks and in multiple languages shows that sequence learning enables more versatile all-words models that consistently lead to state-of-the-art results, even against models trained with engineered features. However, supervised systems need annotated training corpora and the few available to date are of limited size: this is mainly due to the expensive and timeconsuming process of annotating a wide variety of word senses at a reasonably high scale, i.e., the so-called knowledge acquisition bottleneck. To address this issue, we also present different strategies to acquire automatically high quality sense annotated data in multiple languages, without any manual effort. We assess the quality of the sense annotations both intrinsically and extrinsically achieving competitive results on multiple tasks.
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Gorricha, Jorge Manuel Lourenço. "Visualization of clusters in geo-referenced data using three-dimensional self-organizing maps." Master's thesis, 2010. http://hdl.handle.net/10362/2631.

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Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação
The Self-Organizing Map (SOM) is an artificial neural network that performs simultaneously vector quantization and vector projection. Due to this characteristic, the SOM is an effective method for clustering analysis via visualization. The SOM can be visualized through the output space, generally a regular two-dimensional grid of nodes, and through the input space, emphasizing the vector quantization process. Among all the strategies for visualizing the SOM, we are particularly interested in those that allow dealing with spatial dependency, linking the SOM to the geographic visualization with color. One possible approach, commonly used, is the cartographic representation of data with label colors defined from the output space of a two-dimensional SOM. However, in the particular case of geo-referenced data, it is possible to consider the use of a three-dimensional SOM for this purpose, thus adding one more dimension in the analysis. In this dissertation is presented a method for clustering geo-referenced data that integrates the visualization of both perspectives of a three dimensional SOM: linking its output space to the cartographic representation through a ordered set of colors; and exploring the use of frontiers among geo-referenced elements, computed according to the distances in the input space between their Best Matching Units.
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Books on the topic "Frontière neurale"

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Zheng, Xiaoxiang, ed. Neural Interface: Frontiers and Applications. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-2050-7.

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Wu, Lingfei, Peng Cui, Jian Pei, and Liang Zhao, eds. Graph Neural Networks: Foundations, Frontiers, and Applications. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6054-2.

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Levy, Steven. Artificial life: A report from the frontier where computers meet biology. New York: Vintage Books, 1993.

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Markellos, Raphael N. Robust estimation of nonlinear production frontiers and efficiency: A neural network approach. Loughborough: Loughborough University, Department of Economics, 1997.

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Doidge, Norman. The brain that changes itself: Stories of personal triumph from the frontiers of brain science. London: Penguin Books, 2008.

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Neural Frontiers. MDPI, 2023. http://dx.doi.org/10.3390/books978-3-0365-9765-2.

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Zheng, Xiaoxiang. Neural Interface: Frontiers and Applications. Springer, 2019.

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Zheng, Xiaoxiang. Neural Interface: Frontiers and Applications. Springer, 2019.

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Ruthazer, Edward S., and Takao K. Hensch, eds. Frontiers in Neural Circuits - Editors’ Pick 2021. Frontiers Media SA, 2021. http://dx.doi.org/10.3389/978-2-88971-643-2.

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Graph Neural Networks: Foundations, Frontiers, and Applications. Springer, 2023.

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Book chapters on the topic "Frontière neurale"

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Spaanenburg, L., and W. J. Jansen. "Networked Neural Systems." In The Frontiers Collection, 231–42. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22093-2_16.

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Sun, Xiaoan, Sui Huang, and Ningyuan Wang. "Neural Interface: Frontiers and Applications." In Advances in Experimental Medicine and Biology, 167–206. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-2050-7_7.

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Rueckert, Ulrich. "Digital Neural Network Accelerators." In The Frontiers Collection, 181–202. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-18338-7_12.

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Paul, Dorothy H. "Neural Phylogeny — Its Use in Studying Neural Circuits." In Frontiers in Crustacean Neurobiology, 537–46. Basel: Birkhäuser Basel, 1990. http://dx.doi.org/10.1007/978-3-0348-5689-8_67.

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Lin, Eugene, and Shih-Jen Tsai. "Machine Learning in Neural Networks." In Frontiers in Psychiatry, 127–37. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9721-0_7.

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Spaanenburg, Lambert, and Suleyman Malki. "Digital Neural Networks for New Media." In The Frontiers Collection, 331–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23096-7_16.

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Watanabe, Masataka. "Neural Manipulation for Causal Investigation of Consciousness." In The Frontiers Collection, 61–85. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-91138-6_3.

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Filk, Thomas. "The Quantum-like Behavior of Neural Networks." In The Frontiers Collection, 553–75. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92192-7_30.

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Wu, Lingfei, Peng Cui, Jian Pei, Liang Zhao, and Le Song. "Graph Neural Networks." In Graph Neural Networks: Foundations, Frontiers, and Applications, 27–37. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6054-2_3.

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Katz, Jennifer, Bryan Keenan, and Evan Y. Snyder. "Culture and Manipulation of Neural Stem Cells." In Frontiers in Brain Repair, 13–22. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-5819-8_2.

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Conference papers on the topic "Frontière neurale"

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Rakovic, Dejan. "Quantum-informational bases and frontiers of psychosomatic integrative medicine." In 2014 12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL 2014). IEEE, 2014. http://dx.doi.org/10.1109/neurel.2014.7011473.

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Graves, Alex. "Plenary talks: Frontiers in recurrent neural network research." In 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017. http://dx.doi.org/10.1109/ijcnn.2017.7965820.

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Wu, Lingfei, Peng Cui, Jian Pei, Liang Zhao, and Xiaojie Guo. "Graph Neural Networks: Foundation, Frontiers and Applications." In KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3534678.3542609.

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Ji, Shuiwang, Meng Liu, Yi Liu, Youzhi Luo, Limei Wang, Yaochen Xie, Zhao Xu, and Haiyang Yu. "Frontiers of Graph Neural Networks with DIG." In KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3534678.3542624.

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Wu, Lingfei, Peng Cui, Jian Pei, Liang Zhao, and Xiaojie Guo. "Graph Neural Networks: Foundation, Frontiers and Applications." In KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3580305.3599560.

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Gong, Zheng, Carmine Ventre, and John O'Hara. "The efficient hedging frontier with deep neural networks." In ICAIF'21: 2nd ACM International Conference on AI in Finance. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3490354.3494392.

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Zhang, Duzhen, Tielin Zhang, Shuncheng Jia, Qingyu Wang, and Bo Xu. "Recent Advances and New Frontiers in Spiking Neural Networks." 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.2022/790.

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Abstract:
In recent years, spiking neural networks (SNNs) have received extensive attention in brain-inspired intelligence due to their rich spatially-temporal dynamics, various encoding methods, and event-driven characteristics that naturally fit the neuromorphic hardware. With the development of SNNs, brain-inspired intelligence, an emerging research field inspired by brain science achievements and aiming at artificial general intelligence, is becoming hot. This paper reviews recent advances and discusses new frontiers in SNNs from five major research topics, including essential elements (i.e., spiking neuron models, encoding methods, and topology structures), neuromorphic datasets, optimization algorithms, software, and hardware frameworks. We hope our survey can help researchers understand SNNs better and inspire new works to advance this field.
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Baran, Timothy M. "Improved tissue optical property extraction at short source-detector separations using neural networks." In Frontiers in Optics. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/fio.2022.fth3b.2.

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We extracted tissue optical properties from diffuse reflectance data at short source-detector separations using neural networks and Monte Carlo lookup tables. Neural networks significantly improved absorption recovery compared to lookup table (p=0.003).
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Ashtiani, Farshid, Mohamad Hossein Idjadi, Ting-Chen Hu, Stefano Grillanda, David Neilson, Mark Earnshaw, Mark Cappuzzo, Rose Kopf, Alaric Tate, and Andrea Blanco-Redondo. "Neural nonlinearity using a surface normal photodetector for diffractive optical neural networks." In Frontiers in Optics. Washington, D.C.: Optica Publishing Group, 2023. http://dx.doi.org/10.1364/fio.2023.fw6e.2.

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We propose a neural nonlinearity using a surface-normal photodetector with a response time of 5.7 µs and an energy efficiency of < 10 nW/pixel. This device offers a significant improvement in free-space diffractive optical neural networks.
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Greene, Joseph, Yujia Xue, Jeffrey Alido, Alex Matlock, Guorong Hu, Kivilcim Kiliç, Ian Davison, and Lei Tian. "Pupil Engineering in Miniscopes for extended depth-of-field Neural Imaging." In Frontiers in Optics. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/fio.2022.fth3d.4.

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We demonstrate an extended-depth-of-field miniscope (EDoF-Miniscope) for high contrast neural imaging through an optimized binary diffractive optical element (DOE). Our platform achieves a spatial resolution of 0.9-μm and 2.8x axial elongation across fixed neural samples.
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