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Artigos de revistas sobre o assunto "State Token Mechanism"
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Hu, Anwen, Zhicheng Dou, Jian-Yun Nie e Ji-Rong Wen. "Leveraging Multi-Token Entities in Document-Level Named Entity Recognition". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 05 (3 de abril de 2020): 7961–68. http://dx.doi.org/10.1609/aaai.v34i05.6304.
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Most state-of-the-art named entity recognition systems are designed to process each sentence within a document independently. These systems are easy to confuse entity types when the context information in a sentence is not sufficient enough. To utilize the context information within the whole document, most document-level work let neural networks on their own to learn the relation across sentences, which is not intuitive enough for us humans. In this paper, we divide entities to multi-token entities that contain multiple tokens and single-token entities that are composed of a single token. We propose that the context information of multi-token entities should be more reliable in document-level NER for news articles. We design a fusion attention mechanism which not only learns the semantic relevance between occurrences of the same token, but also focuses more on occurrences belonging to multi-tokens entities. To identify multi-token entities, we design an auxiliary task namely ‘Multi-token Entity Classification’ and perform this task simultaneously with document-level NER. This auxiliary task is simplified from NER and doesn't require extra annotation. Experimental results on the CoNLL-2003 dataset and OntoNotesnbm dataset show that our model outperforms state-of-the-art sentence-level and document-level NER methods.
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Yang, Yixiao, Xiang Chen e Jiaguang Sun. "Improve Language Modeling for Code Completion Through Learning General Token Repetition of Source Code with Optimized Memory". International Journal of Software Engineering and Knowledge Engineering 29, n.º 11n12 (novembro de 2019): 1801–18. http://dx.doi.org/10.1142/s0218194019400229.
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In last few years, applying language model to source code is the state-of-the-art method for solving the problem of code completion. However, compared with natural language, code has more obvious repetition characteristics. For example, a variable can be used many times in the following code. Variables in source code have a high chance to be repetitive. Cloned code and templates, also have the property of token repetition. Capturing the token repetition of source code is important. In different projects, variables or types are usually named differently. This means that a model trained in a finite data set will encounter a lot of unseen variables or types in another data set. How to model the semantics of the unseen data and how to predict the unseen data based on the patterns of token repetition are two challenges in code completion. Hence, in this paper, token repetition is modelled as a graph, we propose a novel REP model which is based on deep neural graph network to learn the code toke repetition. The REP model is to identify the edge connections of a graph to recognize the token repetition. For predicting the token repetition of token [Formula: see text], the information of all the previous tokens needs to be considered. We use memory neural network (MNN) to model the semantics of each distinct token to make the framework of REP model more targeted. The experiments indicate that the REP model performs better than LSTM model. Compared with Attention-Pointer network, we also discover that the attention mechanism does not work in all situations. The proposed REP model could achieve similar or slightly better prediction accuracy compared to Attention-Pointer network and consume less training time. We also find other attention mechanism which could further improve the prediction accuracy.
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Kim, Jinsu, Eunsun Choi, Byung-Gyu Kim e Namje Park. "Proposal of a Token-Based Node Selection Mechanism for Node Distribution of Mobility IoT Blockchain Nodes". Sensors 23, n.º 19 (5 de outubro de 2023): 8259. http://dx.doi.org/10.3390/s23198259.
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Various elements, such as evolutions in IoT services resulting from sensoring by vehicle parts and advances in small communication technology devices, have significantly impacted the mass spread of mobility services that are provided to users in need of limited resources. In particular, business models are progressing away from one-off costs towards longer-term costs, as represented by shared services utilizing kick-boards or bicycles and subscription services for vehicle software. Advances in shared mobility services, as described, are calling for solutions that can enhance the reliability of data aggregated by users leveraging mobility services in the next-generation mobility areas. However, the mining process to renew status ensures continued network communication, and block creation demands high performance in the public block chain. Therefore, easing the mining process for state updates in public blockchains is a way to alleviate the high-performance process requirements of public blockchains. The proposed mechanism assigns token-based block creation authority instead of the mining method, which provides block creation authority to nodes that provide many resources. Blocks are created only by a group of participants with tokens, and after creation, tokens are updated and delivered to new nodes to form a new token group. Additionally, tokens are updated in each block after their initial creation, making it difficult to disguise the tokens and preventing resource-centered centralization.
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Bai, He, Peng Shi, Jimmy Lin, Yuqing Xie, Luchen Tan, Kun Xiong, Wen Gao e Ming Li. "Segatron: Segment-Aware Transformer for Language Modeling and Understanding". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 14 (18 de maio de 2021): 12526–34. http://dx.doi.org/10.1609/aaai.v35i14.17485.
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Transformers are powerful for sequence modeling. Nearly all state-of-the-art language models and pre-trained language models are based on the Transformer architecture. However, it distinguishes sequential tokens only with the token position index. We hypothesize that better contextual representations can be generated from the Transformer with richer positional information. To verify this, we propose a segment-aware Transformer (Segatron), by replacing the original token position encoding with a combined position encoding of paragraph, sentence, and token. We first introduce the segment-aware mechanism to Transformer-XL, which is a popular Transformer-based language model with memory extension and relative position encoding. We find that our method can further improve the Transformer-XL base model and large model, achieving 17.1 perplexity on the WikiText-103 dataset. We further investigate the pre-training masked language modeling task with Segatron. Experimental results show that BERT pre-trained with Segatron (SegaBERT) can outperform BERT with vanilla Transformer on various NLP tasks, and outperforms RoBERTa on zero-shot sentence representation learning. Our code is available on GitHub.
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Sitnik, A. A. "NFT as an Object of Legal Regulation". Actual Problems of Russian Law 17, n.º 12 (19 de novembro de 2022): 84–93. http://dx.doi.org/10.17803/1994-1471.2022.145.12.084-093.
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The paper is devoted to the study of the legal nature of a non-fungible token — NFT. The paper discusses the concept and types of tokens. The author defines a token as a unit of accounting in a distributed ledger that digitally represents financial instruments or other assets that expresses the economic value of the objects being represented and allows the rights associated with them to be exercised. According to a common point of view, NFT serves as a means of digital expression of a particular object, it has characteristics (signs) inherent exclusively to it, by virtue of which it cannot be exchanged for another token, and the cost of one NFT is not conditioned by the cost of other tokens. The author notes that the listed features are not inherent in NFT in all cases. In addition, using the example of NFT, the author draws attention to the problem of artificial limitations of the mechanism of legal regulation of fundamentally new digital objects. It is determined that, with regard to NFT, today in the Russian Federation, both the legislator and the financial market regulator maintain the status quo: the state intervenes in public relations that develop during the turnover of non-fungible tokens only if transactions involving them violate the law. Meanwhile, it can be expected that eventually the problems of the issue and circulation of NFT in the financial market will receive their regulatory and legal resolution.
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Huang, Lingbo, Yushi Chen e Xin He. "Spectral-Spatial Mamba for Hyperspectral Image Classification". Remote Sensing 16, n.º 13 (3 de julho de 2024): 2449. http://dx.doi.org/10.3390/rs16132449.
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Recently, transformer has gradually attracted interest for its excellence in modeling the long-range dependencies of spatial-spectral features in HSI. However, transformer has the problem of the quadratic computational complexity due to the self-attention mechanism, which is heavier than other models and thus has limited adoption in HSI processing. Fortunately, the recently emerging state space model-based Mamba shows great computational efficiency while achieving the modeling power of transformers. Therefore, in this paper, we first proposed spectral-spatial Mamba (SS-Mamba) for HSI classification. Specifically, SS-Mamba mainly includes a spectral-spatial token generation module and several stacked spectral-spatial Mamba blocks. Firstly, the token generation module converts any given HSI cube to spatial and spectral tokens as sequences. And then these tokens are sent to stacked spectral-spatial mamba blocks (SS-MB). Each SS-MB includes two basic mamba blocks and a spectral-spatial feature enhancement module. The spatial and spectral tokens are processed separately by the two basic mamba blocks, correspondingly. Moreover, the feature enhancement module modulates spatial and spectral tokens using HSI sample’s center region information. Therefore, the spectral and spatial tokens cooperate with each other and achieve information fusion within each block. The experimental results conducted on widely used HSI datasets reveal that the proposed SS-Mamba requires less processing time compared with transformer. The Mamba-based method thus opens a new window for HSI classification.
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Liu, Huey-Ing, e Wei-Lin Chen. "X-Transformer: A Machine Translation Model Enhanced by the Self-Attention Mechanism". Applied Sciences 12, n.º 9 (29 de abril de 2022): 4502. http://dx.doi.org/10.3390/app12094502.
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Machine translation has received significant attention in the field of natural language processing not only because of its challenges but also due to the translation needs that arise in the daily life of modern people. In this study, we design a new machine translation model named X-Transformer, which refines the original Transformer model regarding three aspects. First, the model parameter of the encoder is compressed. Second, the encoder structure is modified by adopting two layers of the self-attention mechanism consecutively and reducing the point-wise feed forward layer to help the model understand the semantic structure of sentences precisely. Third, we streamline the decoder model size, while maintaining the accuracy. Through experiments, we demonstrate that having a large number of decoder layers not only affects the performance of the translation model but also increases the inference time. The X-Transformer reaches the state-of-the-art result of 46.63 and 55.63 points in the BiLingual Evaluation Understudy (BLEU) metric of the World Machine Translation (WMT), from 2014, using the English–German and English–French translation corpora, thus outperforming the Transformer model with 19 and 18 BLEU points, respectively. The X-Transformer significantly reduces the training time to only 1/3 times that of the Transformer. In addition, the heat maps of the X-Transformer reach token-level precision (i.e., token-to-token attention), while the Transformer model remains at the sentence level (i.e., token-to-sentence attention).
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Guo, Chaopeng, Pengyi Zhang, Bangyao Lin e Jie Song. "A Dual Incentive Value-Based Paradigm for Improving the Business Market Profitability in Blockchain Token Economy". Mathematics 10, n.º 3 (29 de janeiro de 2022): 439. http://dx.doi.org/10.3390/math10030439.
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Blockchain solves the problem of mutual trust and consensus in the business market of the token economy. In the existing paradigm of blockchain token economy, there are disadvantages of lacking the incentive mechanism, business applications and virtual token value. These shortcomings reduce consumers’ willingness to consume and the profits of the merchants. In this paper, we propose a novel “Dual incentive value-based” paradigm to improve the business market profitability in blockchain token economy. To evaluate our proposed paradigm, we propose a business study case for improving merchants’ environment state. In this case, we set up two economic models and make simulations to validate the profitability. The result shows that merchants with the novel paradigm have 32% more profit compared with those without the paradigm and at most 10% more profitable than those in existing paradigms.
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Khoo, Ling Min Serena, Hai Leong Chieu, Zhong Qian e Jing Jiang. "Interpretable Rumor Detection in Microblogs by Attending to User Interactions". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 05 (3 de abril de 2020): 8783–90. http://dx.doi.org/10.1609/aaai.v34i05.6405.
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We address rumor detection by learning to differentiate between the community's response to real and fake claims in microblogs. Existing state-of-the-art models are based on tree models that model conversational trees. However, in social media, a user posting a reply might be replying to the entire thread rather than to a specific user. We propose a post-level attention model (PLAN) to model long distance interactions between tweets with the multi-head attention mechanism in a transformer network. We investigated variants of this model: (1) a structure aware self-attention model (StA-PLAN) that incorporates tree structure information in the transformer network, and (2) a hierarchical token and post-level attention model (StA-HiTPLAN) that learns a sentence representation with token-level self-attention. To the best of our knowledge, we are the first to evaluate our models on two rumor detection data sets: the PHEME data set as well as the Twitter15 and Twitter16 data sets. We show that our best models outperform current state-of-the-art models for both data sets. Moreover, the attention mechanism allows us to explain rumor detection predictions at both token-level and post-level.
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Keerthana, R. L., Awadhesh Kumar Singh, Poonam Saini e Diksha Malhotra. "Explaining Sarcasm of Tweets using Attention Mechanism". Scalable Computing: Practice and Experience 24, n.º 4 (17 de novembro de 2023): 787–96. http://dx.doi.org/10.12694/scpe.v24i4.2166.
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Emotion identification from text can help boost the effectiveness of sentiment analysis models. Sarcasm is one of the more difficult emotions to detect, particularly in textual data. Even though several models for detecting sarcasm have been presented, their performance falls way short of that of other emotion detection models. As a result, few strategies have been introduced in the paper that helped to enhance the performance of sarcasm detection models. To compare performance, the model was tested using the TweetEval benchmark dataset. On the TweetEval benchmark, the technique proposed in this paper has established a new state-of-the-art. Besides the low performance, interpretability of existing sarcasm detection models are lacking compared to other emotion detection models like hate speech and anger. Therefore, an attention-based interpretability technique has been proposed in this paper that interprets the token importance for a certain decision of sarcasm detection model. The results of the interpretability technique aid in our comprehension of the contextual embeddings of the input tokens that the model has paid the greatest attention to while making a particular decision which outperforms existing transformer-based interpretability techniques, particularly in terms of visualisations.
Teses / dissertações sobre o assunto "State Token Mechanism"
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Kady, Charbel. "Managing Business Process Continuity and Integrity Using Pattern-Based Corrections". Electronic Thesis or Diss., IMT Mines Alès, 2024. http://www.theses.fr/2024EMAL0014.
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Cette thèse présente une approche pour la gestion des déviations dans les flux de travail utilisant le Business Process Model and Notation (BPMN). La recherche répond au besoin de gestion efficace des déviations en intégrant un cadre complet comprenant la correction des déviations basée sur des modèles et un mécanisme enrichi de State Token. L’approche est testée par une étude de cas dans le domaine de l’apiculture, démontrant l’applicabilité pratique et l’efficacité de la méthode proposée. Les contributions clés incluent le développement d’une bibliothèque de modèles, la caractérisation des éléments BPMN et un mécanisme pour aider à la prise de décision dans la gestion des déviations. Les résultats montrent que l’approche peut corriger efficacement les déviations, assurant la continuité et l’intégrité du flux de travailThis thesis presents an approach to managing deviations in Business Process Model and Notation (BPMN) workflows. The research addresses the critical need for effective deviation management by integrating a comprehensive framework that includes pattern-based deviation correction and an enriched State Token mechanism. The approach is tested through a case study in the apiculture domain, demonstrating the practical applicability and effectiveness of the proposed method. Key contributions include the development of a library of patterns, the characterization of BPMN elements, and a mechanism to help decision-making in addressing deviations. The results show that the approach can efficiently correct deviations, ensuring workflow continuity and integrity
Capítulos de livros sobre o assunto "State Token Mechanism"
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Ante, Lennart. "Blockchain-Based Tokens as Financing Instruments". In Fostering Innovation and Competitiveness With FinTech, RegTech, and SupTech, 129–41. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4390-0.ch007.
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Blockchain technology represents a technological basis with which existing corporate financing processes can be supplemented. The issuance of digital tokens offers several potential advantages such as tradability, efficiency, automation, and cost benefits compared to traditional financial products. This transformation of financing processes and capital markets can allow small and medium-sized enterprises (SMEs) to access capital markets and at the same time close existing retail investment gaps. In this chapter, the challenges of SME financing are described and blockchain-based financing (initial coin offerings [ICOs] and security token offerings [STOs]) is introduced. The blockchain-based financing mechanisms are compared with conventional forms of financing and potentials and challenges are discussed. In conclusion, it is stated that potential clearly outweighs risk and that the majority of all existing challenges can be tackled through sensible and coordinated regulation.
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Lipton, Alexander. "Toward a Stable Tokenized Medium of Exchange". In Cryptoassets, 89–116. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190077310.003.0005.
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This chapter discusses the current state of the crypto land and argues that stable crypto tokens, which can be viewed as an electronic analogue of cash, can help augment the existing TCP/IP (Transition Control Protocal/Internet Protocol) with a much-needed mechanism in order to bring existing banking and payment systems into the twenty-first century. It describes three existing approaches to designing such tokens—fiat collateralization, cryptocurrency collateralization, and dynamic stabilization—and concludes that only regulatorily compliant fiat-backed tokens are viable in the long run. It also discusses asset-backed cryptocurrencies and argues that in some instances they can provide a much-needed counterpoint for today's fiat currencies, and pave a way forward toward ensuring world-wide financial stability and inclusion.
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Włosik, Katarzyna. "Initial coin offering jako nowa forma finansowania i inwestycji". In Innowacje finansowe w gospodarce 4.0, 70–87. Wydawnictwo Uniwersytetu Ekonomicznego w Poznaniu, 2021. http://dx.doi.org/10.18559/978-83-8211-083-8/4.
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This part of the monograph is related to initial coin offering – a mechanism that allows blockchain-based companies or projects to obtain financing. In return for financial support, ICO participants are offered different types of digital tokens – payment, utility or investment tokens. The chapter contains the systematization of issues related to ICO and tokens as well as a description of stages of initial coin offering. The SWOT analysis of ICO highlights the strengths and opportunities related to ICO – inter alia the possibility of portfolio diversification and the limited access for individual investors to early-stage investments (apart from ICO). Also the weaknesses of initial coin offering (e.g. the need to prepare a due diligence by an investor) and associated risks (e.g. regulatory uncertainty) are considered. Moreover, the author identifies research areas related to ICO. They include, among others, the identification of ICO success factors and the identification of factors affecting the rates of return on tokens.
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Sai Swaroop, Akella, e S. Rama Sree. "Network Mechanism Establishment and Authentication Using Digital Certificate Management". In Advances in Transdisciplinary Engineering. IOS Press, 2023. http://dx.doi.org/10.3233/atde221270.
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The fundamental idea behind all types of data frameworks is authentication, however targeted single-side validation is thought to be weak and vulnerable, posing a security risk of single-side failure or breakdown brought on by external attacks or internal fraud. In this paper, we proposed a blockchain-based decentralized validation illustrating plan (named BlockAuth) for the edge and IoT environments to provide a safer, dependable, and solid acclimation to non-critical failure arrangement, where each edge device is viewed as a hub to construct a blockchain network. We designed a safe sign-up and verification process, and the decentralized blockchain confirmation convention encouraged blockchain agreement and brilliant agreement. We also carried out a full blockchain-based validation stage for the evaluation of practicality, security, and execution. With a considerable level of protection setup for the executives, the evaluation and examination indicate that the suggested BlockAuth plot offers a safer, dependable, and solid adaptability to internal failure decentralized new verification. The suggested BlockAuth scheme is suitable for a token-positioned secret word, testament, and Verification is anticipated for unquestionably high security requirements in the edge & IoT environment.
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Li, Wenda, Kaixuan Chen, Shunyu Liu, Tongya Zheng, Wenjie Huang e Mingli Song. "Learning a Mini-Batch Graph Transformer via Two-Stage Interaction Augmentation". In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240842.
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Mini-batch Graph Transformer (MGT), as an emerging graph learning model, has demonstrated significant advantages in semi-supervised node prediction tasks with improved computational efficiency and enhanced model robustness. However, existing methods for processing local information either rely on sampling or simple aggregation, which respectively result in the loss and squashing of critical neighbor information. Moreover, the limited number of nodes in each mini-batch restricts the model’s capacity to capture the global characteristic of the graph. In this paper, we propose LGMformer, a novel MGT model that employs a two-stage augmented interaction strategy, transitioning from local to global perspectives, to address the aforementioned bottlenecks. The local interaction augmentation (LIA) presents a neighbor-target interaction Transformer (NTIformer) to acquire an insightful understanding of the co-interaction patterns between neighbors and the target node, resulting in a locally effective token list that serves as input for the MGT. In contrast, global interaction augmentation (GIA) adopts a cross-attention mechanism to incorporate entire graph prototypes into the target node representation, thereby compensating for the global graph information to ensure a more comprehensive perception. To this end, LGMformer achieves the enhancement of node representations under the MGT paradigm. Experimental results related to node classification on the ten benchmark datasets demonstrate the effectiveness of the proposed method. Our code is available at https://github.com/l-wd/LGMformer.
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"Introduction to Financial Digital Assets". In Financial Digital Assets and the Financial Risk Modeling of Portfolio Investments, 1–50. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-8120-5.ch001.
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The chapter provides a comprehensive overview of the evolving landscape of digital assets, setting the stage for a deeper exploration of their impact on the financial industry. This chapter begins by defining digital assets, encompassing cryptocurrencies like Bitcoin and Ethereum, tokenized securities, and non-fungible tokens (NFTs). It delves into the foundational technologies underpinning these assets, such as blockchain and distributed ledger technology, highlighting their role in ensuring transparency, security, and decentralization. The chapter also examines the historical context and evolution of digital assets, tracing their journey from niche innovations to mainstream financial instruments. Key concepts such as decentralization, tokenization, and smart contracts are introduced, providing readers with a solid understanding of the mechanisms driving the digital asset ecosystem.
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Wu, Junjie, Mingjie Sun, Chen Gong, Nan Yu e Guohong Fu. "PromptCD: Coupled and Decoupled Prompt Learning for Vision-Language Models". In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240504.
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Large-scale pre-trained vision-language models (VLMs), like CLIP, have presented striking generalizability for adapting to image classification in a few shot setting. Most existing methods explore a set of learnable tokens, such as prompt learning, on data-efficient utilization for task adaptation. However, they focus on either the coupled-modality property by prompt projection or decoupled-modality characteristic by prompt consistency, which ignores effective interaction between prompts. To model the deep yet sufficient cross-modal interaction and enhance the generalization between both seen and unseen tasks, in this paper, we propose a novel coupled and decoupled prompt learning framework, dubbed PromptCD, for vision-language models. Specifically, we introduce a bi-directional coupled-modality mechanism to intensify the interaction between both vision and language branches. Additionally, we propose mixture consistency to further improve the generalization and discrimination of the models on unseen tasks. The integration of such a mechanism and consistency facilitates the proposed framework adaptation for various downstream tasks. We conduct extensive experiments on 11 image classification datasets under a range of evaluation protocols, including base-to-novel and domain generalization, and cross-dataset recognition. Experimental results demonstrate that our proposed PromptCD overall outperforms state-of-the-art methods.
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Fagan, Melinda Bonnie. "Stem cells". In Routledge Encyclopedia of Philosophy. London: Routledge, 2023. http://dx.doi.org/10.4324/9780415249126-q152-1.
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What is a stem cell? The term is a combination of ‘cell’ and ‘stem’. A cell is a major category of living thing, while a stem is a site of growth and support for something else. In science today, a stem cell is defined as a cell derived from a multicellular organism, which is able to both self-renew (produce more stem cells of the same kind) and differentiate (produce cells corresponding to later developmental stages of the source organism). So the concept of a stem cell is somewhat complex, bearing on questions of biological individuality, relations between cells and organisms, and our understanding of development. Stem cell phenomena range from everyday to extraordinary laboratory products. On the everyday side: hair, skin, and blood cells are shed and replaced by ongoing stem cell activities. Stem cells help maintain organs and tissues in mature multicellular organisms. Regeneration in wound-healing is also, often, stem-cell mediated. The hydra’s mythic regeneration potential is due to its plentiful stem cells; similarly for plants. Looking to earlier developmental stages, embryonic cells also exhibit stem cell capacities. If such cells are removed from an early embryo and grown in artificial cell culture, this produces an embryonic stem cell line – an indefinitely renewable source of cells that can, under appropriate conditions, develop to produce many (even all) cell types found in a mature organism. Other experimental products of stem cells include embryoid bodies, organoids, and embryo-like structures. Stem cells are thus found in living organisms (in vivo) and grown artificially (in vitro). Stem cells raise several important metaphysical questions for philosophers of biology. One concerns biological individuality. Multicellular organisms are paradigmatic biological individuals. There are strong reasons to think cells are individuals. Stem cells are cells that divide and develop into other kinds of cell, tissues, organs, and even analogues of whole organisms. Are stem cells individuals? One way to answer this question is in terms of cell lineages. Complicating matters, stem cells mediate between cell and organismal levels of biological organisation. This raises questions about individuality and development for organisms and constituent cell lineages. Metaphysical theories about the nature of stem cells – natural kinds, causal mechanisms, processes – are also unsettled, as is the science. Different metaphysical theories about the nature of stem cells present a problem of theory choice. Alternatives include: stem cells as entities, stemness as a state, disposition to develop, and cell-environment systems. Our knowledge about stem cells is incomplete, based on many different kinds of experiment. The main ways of identifying stem cells are to find, grow, or make them: cell-sorting, in vitro culture, and reprogramming, respectively. The basic design is to remove cells from an organismal source and place them in an environment where they can self-renew. After measuring cell traits in this environment, some cells are moved to a new environment to encourage differentiation. Cell traits in the new environment are then measured. The results correlate traits of an organismal source, candidate stem cells, and differentiated cells. Collectively, these experiments yield many different varieties of stem cell. Characterisation of these varieties is closely tied to technologies and experimental methods for culturing, visualising, and manipulating cells. Uncertainty is a constant, however. It’s impossible to experimentally show that a single cell is a stem cell; all methods of identifying stem cells require populations of homogeneous stem cells. But homogeneity for cells that by definition transform into other things is a fragile assumption. Consequently, stem cells are identified relative to particular experimental methods. Our knowledge of stem cells accumulates by multiplying experimental contexts and relating their outcomes to one another. In practice, knowledge about stem cells has the form of a proliferating network of models. In vitro stem cells are a prominent example: concrete approximations of early developmental stages of a multicellular organism of a particular species. Other important stem cell-based models are organoids and human-animal chimeras. Different stem cell models complement one another, highlighting different aspects of development. More generally, stem cell biology is replete with abstract and concrete models. Social organisation of experiments and resultant models is important for understanding the epistemology of stem cell research. Abstract models play a less prominent role in stem cell research, although lineage tree models are important representations of stem cells and their potential. Classifying stem cells is an unsettled and messy affair, with many different cross-cutting or overlapping distinctions used in practice. There are many varieties of stem cell, but no single agreed-upon system for classifying them. Lineage tree models offer one prospect for such a system. In popular culture, stem cells are associated with medical promise on the one hand, and embryo destruction on the other. Stem cells are tokens of medical promise and hope; the idea being to use their potential to cure a wide range of injuries and diseases. This promise motivates stem cell ‘clinics’ alongside scientific research. The former peddle cures for many ailments unencumbered by scientific evidence or regulatory approval. The latter challenged by ethical questions about human embryo research. Tension between medical hopes and objections to human embryo research has produced a large bioethics literature. Key ethical debates are about research using human embryos, creating human–animal chimeras, and how to balance hope and hype in regulating and funding stem cell research. Broad anti-science cultural movements encourage proliferation of stem cell ‘clinics’ that market alleged cures directly to consumers, bypassing scientific and medical standards.
Trabalhos de conferências sobre o assunto "State Token Mechanism"
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Zhou, Yan, Longtao Huang, Tao Guo, Jizhong Han e Songlin Hu. "A Span-based Joint Model for Opinion Target Extraction and Target Sentiment Classification". In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/762.
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Target-Based Sentiment Analysis aims at extracting opinion targets and classifying the sentiment polarities expressed on each target. Recently, token based sequence tagging methods have been successfully applied to jointly solve the two tasks, which aims to predict a tag for each token. Since they do not treat a target containing several words as a whole, it might be difficult to make use of the global information to identify that opinion target, leading to incorrect extraction. Independently predicting the sentiment for each token may also lead to sentiment inconsistency for different words in an opinion target. In this paper, inspired by span-based methods in NLP, we propose a simple and effective joint model to conduct extraction and classification at span level rather than token level. Our model first emulates spans with one or more tokens and learns their representation based on the tokens inside. And then, a span-aware attention mechanism is designed to compute the sentiment information towards each span. Extensive experiments on three benchmark datasets show that our model consistently outperforms the state-of-the-art methods.
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Yin, Shi, Shijie Huang, Shangfei Wang, Jinshui Hu, Tao Guo, Bing Yin, Baocai Yin e Cong Liu. "1DFormer: A Transformer Architecture Learning 1D Landmark Representations for Facial Landmark Tracking". In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. California: International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/176.
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Recently, heatmap regression methods based on 1D landmark representations have shown prominent performance on locating facial landmarks. However, previous methods ignored to make deep explorations on the good potentials of 1D landmark representations for sequential and structural modeling of multiple landmarks to track facial landmarks. To address this limitation, we propose a Transformer architecture, namely 1DFormer, which learns informative 1D landmark representations by capturing the dynamic and the geometric patterns of landmarks via token communications in both temporal and spatial dimensions for facial landmark tracking. For temporal modeling, we propose a confidence-enhanced multi-head attention mechanism with a recurrently token mixing strategy to adaptively and robustly embed long-term landmark dynamics into their 1D representations; for structure modeling, we design intra-group and inter-group geometric encoding mechanisms to encode the component-level as well as global-level facial structure patterns as a refinement for the 1D representations of landmarks through token communications in the spatial dimension via 1D convolutional layers. Experimental results on the 300VW and the TF databases show that 1DFormer successfully models the long-range sequential patterns as well as the inherent facial structures to learn informative 1D representations of landmark sequences, and achieves state-of-the-art performance on facial landmark tracking. Codes of our model are available in the supplementary materials.
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Liu, Jie, Shaowei Chen, Bingquan Wang, Jiaxin Zhang, Na Li e Tong Xu. "Attention as Relation: Learning Supervised Multi-head Self-Attention for Relation Extraction". In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/524.
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Joint entity and relation extraction is critical for many natural language processing (NLP) tasks, which has attracted increasing research interest. However, it is still faced with the challenges of identifying the overlapping relation triplets along with the entire entity boundary and detecting the multi-type relations. In this paper, we propose an attention-based joint model, which mainly contains an entity extraction module and a relation detection module, to address the challenges. The key of our model is devising a supervised multi-head self-attention mechanism as the relation detection module to learn the token-level correlation for each relation type separately. With the attention mechanism, our model can effectively identify overlapping relations and flexibly predict the relation type with its corresponding intensity. To verify the effectiveness of our model, we conduct comprehensive experiments on two benchmark datasets. The experimental results demonstrate that our model achieves state-of-the-art performances.
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Theil, Christoph Kilian, Samuel Broscheit e Heiner Stuckenschmidt. "PRoFET: Predicting the Risk of Firms from Event Transcripts". In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/724.
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Financial risk, defined as the chance to deviate from return expectations, is most commonly measured with volatility. Due to its value for investment decision making, volatility prediction is probably among the most important tasks in finance and risk management. Although evidence exists that enriching purely financial models with natural language information can improve predictions of volatility, this task is still comparably underexplored. We introduce PRoFET, the first neural model for volatility prediction jointly exploiting both semantic language representations and a comprehensive set of financial features. As language data, we use transcripts from quarterly recurring events, so-called "earnings calls"; in these calls, the performance of publicly traded companies is summarized and prognosticated by their management. We show that our proposed architecture, which models verbal context with an attention mechanism, significantly outperforms the previous state-of-the-art and other strong baselines. Finally, we visualize this attention mechanism on the token-level, thus aiding interpretability and providing a use case of PRoFET as a tool for investment decision support.
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Zhou, Chengjie, Chao Che, Pengfei Wang e Qiang Zhang. "SCAT: A Time Series Forecasting with Spectral Central Alternating Transformers". In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. California: International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/622.
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Time series forecasting has essential applications across various domains. For instance, forecasting power time series can optimize energy usage and bolster grid stability and reliability. Existing models based on transformer architecture are limited to classical design, ignoring the impact of spatial information and noise on model architecture design. Therefore, we propose an atypical design of Transformer-based models for multivariate time series forecasting. This design consists of two critical components: (i) spectral clustering center of time series employed as the focal point for attention computation; (ii) alternating attention mechanism wherein each query transformer is compatible with spectral clustering centers, executing attention at the sequence level instead of the token level. The alternating design has a two-fold benefit: firstly, it eliminates the uncertainty noise present in the dependent variable sequence of the channel input, and secondly, it incorporates the Euclidean distance to mitigate the impact of extreme values on the attention matrix, thereby aligning predictions more closely to the sequence's natural progression. Experiments on ten real-world datasets, encompassing Wind, Electricity, Weather, and others, demonstrate that our Spectral Central Alternating Transformer (SCAT) outperforms state-of-the-art methods (SOTA) by an average of 42.8% in prediction performance in power time series forecasting.
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Shen, Tao, Tianyi Zhou, Guodong Long, Jing Jiang, Sen Wang e Chengqi Zhang. "Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling". In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/604.
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Many natural language processing tasks solely rely on sparse dependencies between a few tokens in a sentence. Soft attention mechanisms show promising performance in modeling local/global dependencies by soft probabilities between every two tokens, but they are not effective and efficient when applied to long sentences. By contrast, hard attention mechanisms directly select a subset of tokens but are difficult and inefficient to train due to their combinatorial nature. In this paper, we integrate both soft and hard attention into one context fusion model, "reinforced self-attention (ReSA)", for the mutual benefit of each other. In ReSA, a hard attention trims a sequence for a soft self-attention to process, while the soft attention feeds reward signals back to facilitate the training of the hard one. For this purpose, we develop a novel hard attention called "reinforced sequence sampling (RSS)", selecting tokens in parallel and trained via policy gradient. Using two RSS modules, ReSA efficiently extracts the sparse dependencies between each pair of selected tokens. We finally propose an RNN/CNN-free sentence-encoding model, "reinforced self-attention network (ReSAN)", solely based on ReSA. It achieves state-of-the-art performance on both the Stanford Natural Language Inference (SNLI) and the Sentences Involving Compositional Knowledge (SICK) datasets.
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Zhang, Wenchang, Hua Wang e Fan Zhang. "Skip-Timeformer: Skip-Time Interaction Transformer for Long Sequence Time-Series Forecasting". In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. California: International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/608.
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Recent studies have raised questions about the suitability of the Transformer architecture for long sequence time-series forecasting. These forecasting models leverage Transformers to capture dependencies between multiple time steps in a time series, with embedding tokens composed of data from individual time steps. However, challenges arise when applying Transformers to predict long sequences with strong periodicity, leading to performance degradation and increased computational burden. Furthermore, embedding tokens formed one time step at a time may struggle to reveal meaningful information in long sequences, failing to capture correlations between different time steps. In this study, we propose Skip-Timeformer, a Transformer-based model that utilizes a skip-time interaction for long sequence time-series forecasting. Specifically, we decompose the time series into multiple subsequences based on different time intervals, embedding various time steps into variable tokens across multiple sequences. The skip-time interaction mechanism utilizes these variable tokens to capture dependencies in the skip-time dimension. Additionally, skip-time interaction is employed to learn dependencies between sequences missed by multiple skip time steps. The Skip-Timeformer model demonstrates state-of-the-art performance on various real-world datasets, further enhancing the long sequence forecasting capabilities of the Transformer variations and better adapting to arbitrary lookback windows.
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Kahatapitiya, Kumara, e Michael S. Ryoo. "SWAT: Spatial Structure Within and Among Tokens". In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/106.
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Modeling visual data as tokens (i.e., image patches) using attention mechanisms, feed-forward networks or convolutions has been highly effective in recent years. Such methods usually have a common pipeline: a tokenization method, followed by a set of layers/blocks for information mixing, both within and among tokens. When image patches are converted into tokens, they are often flattened, discarding the spatial structure within each patch. As a result, any processing that follows (eg: multi-head self-attention) may fail to recover and/or benefit from such information. In this paper, we argue that models can have significant gains when spatial structure is preserved during tokenization, and is explicitly used during the mixing stage. We propose two key contributions: (1) Structure-aware Tokenization and, (2) Structure-aware Mixing, both of which can be combined with existing models with minimal effort. We introduce a family of models (SWAT), showing improvements over the likes of DeiT, MLP-Mixer and Swin Transformer, across multiple benchmarks including ImageNet classification and ADE20K segmentation. Our code is available at github.com/kkahatapitiya/SWAT.
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Liu, Zicheng, Li Wang, Siyuan Li, Zedong Wang, Haitao Lin e Stan Z. Li. "LongVQ: Long Sequence Modeling with Vector Quantization on Structured Memory". In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. California: International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/510.
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Transformer models have been successful in various sequence processing tasks, but the self-attention mechanism's computational cost limits its practicality for long sequences. Although there are existing attention variants that improve computational efficiency, they have a limited ability to abstract global information effectively based on their hand-crafted mixing strategies. On the other hand, state-space models (SSMs) are tailored for long sequences but cannot capture complicated local information. Therefore, the combination of them as a unified token mixer is a trend in recent long-sequence models. However, the linearized attention degrades performance significantly even when equipped with SSMs. To address the issue, we propose a new method called LongVQ. LongVQ uses the vector quantization (VQ) technique to compress the global abstraction as a length-fixed codebook, enabling the linear-time computation of the attention matrix. This technique effectively maintains dynamic global and local patterns, which helps to complement the lack of long-range dependency issues. Our experiments on the Long Range Arena benchmark, autoregressive language modeling, and image and speech classification demonstrate the effectiveness of LongVQ. Our model achieves significant improvements over other sequence models, including variants of Transformers, Convolutions, and recent State Space Models.
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Yan, Fan, e Ming Li. "Towards Generating Summaries for Lexically Confusing Code through Code Erosion". In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/512.
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Code summarization aims to summarize code functionality as high-level nature language descriptions to assist in code comprehension. Recent approaches in this field mainly focus on generating summaries for code with precise identifier names, in which meaningful words can be found indicating code functionality. When faced with lexically confusing code, current approaches are likely to fail since the correlation between code lexical tokens and summaries is scarce. To tackle this problem, we propose a novel summarization framework named VECOS. VECOS introduces an erosion mechanism to conquer the model's reliance on precisely defined lexical information. To facilitate learning the eroded code's functionality, we force the representation of the eroded code to align with the representation of its original counterpart via variational inference. Experimental results show that our approach outperforms the state-of-the-art approaches to generate coherent and reliable summaries for various lexically confusing code.