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检索条件"丛书名=Lecture notes in artificial intelligence,"

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3D Reconstructions of Brain from MRI Scans Using Neural Radiance Fields 22nd

3D Reconstructions of Brain from MRI Scans Using Neural Radi...

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22nd International Conference on artificial intelligence and Soft Computing (ICAISC)

作者： Iddrisu, Khadija Malec, Sylwia Crimi, Alessandro African Inst Math Sci Cape Town South Africa Sano Ctr Computat Med Krakow Poland

ISBN: (纸本)9783031425073;9783031425080

The advent of 3D Magnetic Resonance Imaging (MRI) has revolutionized medical imaging and diagnostic capabilities, allowing for more precise diagnosis, treatment planning, and improved patient outcomes. 3D MRI imaging enables the creation of detailed 3D reconstructions of anatomical structures that can be used for visualization, analysis, and surgical planning. However, these reconstructions often require many scan acquisitions, demanding a long session to use the machine and requiring the patient to remain still, with consequent possible motion artifacts. The development of neural radiance fields (NeRF) technology has shown promising results in generating highly accurate 3D reconstructions of MRI images with less user input. Our approach is based on neural radiance fields to reconstruct 3D projections from 2D slices of MRI scans. We do this by using 3D convolutional neural networks to address challenges posed by variable slice thickness;incorporating multiple MRI modalities to ensure robustness and extracting the shape and volumetric depth of both surface and internal anatomical structures with slice interpolation. This approach provides more comprehensive and robust 3D reconstructions of both surface and internal anatomical structures and has significant potential for clinical applications, allowing medical professionals to better visualize and analyze anatomical structures with less available data, potentially reducing times and motion-related issues.

关键词： 3D reconstruction MRI scans Neural radiance fields Structure from motion

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Evaluating the Causal Role of Environmental Data in Shellfish Biotoxin Contamination on the Portuguese Coast 22nd

Evaluating the Causal Role of Environmental Data in Shellfis...

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22nd EPIA Conference on artificial intelligence (EPIA)

作者： Baiao, Ana Rita Peixoto, Carolina Lopes, Marta B. Costa, Pedro Reis Carvalho, Alexandra M. Vinga, Susana Univ Lisbon Inst Super Tecn INESC ID P-1000029 Lisbon Portugal NOVA FCT Dept Math Ctr Math & Applicat NOVA Math P-2829516 Caparica Portugal NOVA FCT Dept Mech & Ind Engn UNIDEMI P-2829516 Caparica Portugal IPMA Inst Portugues Mar & Atmosfera P-1495006 Lisbon Portugal Inst Telecomunicacoes Ave Rovisco Pais 1 P-1049001 Lisbon Portugal Inst Super Tecn Ave Rovisco Pais 1 P-1049001 Lisbon Portugal

ISBN: (纸本)9783031490101;9783031490118

Shellfish accumulation of marine biotoxins at levels unsafe for human consumption may severely impact their harvesting and farming, which has been grown worldwide in response to the growing demand for nutritious food and protein sources. In Southern European countries, diarrhetic shellfish poisoning (DSP) toxins are the most abundant and frequent toxins derived from algal blooms, affecting shellfish production yearly. Therefore, it is essential to understand the natural phenomenon of DSP toxins accumulation in shellfish and the meteorological and biological parameters that may regulate and influence its occurrence. In this work, we studied the relationship between the time series of several meteorological and biological variables and the time series of the concentration of DSP toxins in mussels on the Portuguese coast, using the Pearson's correlation coefficient, time series regression modeling, Granger causality, and dynamic Bayesian networks using the MAESTRO tool. The results show that, for the models tested, the mean sea surface and air temperature time series with a one, two, or three-week lag can be valuable candidate predictors for forecasting the DSP concentration in mussels. Overall, this proof-of-concept study emphasizes the importance of statistical learning methodologies for analyzing time series environmental data and illustrates the importance of several variables in predicting DSP biotoxins concentration, which can help the shellfish production sector mitigate the negative impacts of DSP biotoxins accumulation.

关键词： Shellfish contamination Harmful algal blooms Time series regression modeling Dynamic Bayesian Networks

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Some Analytic Systems of Rules 32nd

Some Analytic Systems of Rules

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32nd International Conference on Automated Reasoning with Analytic Tableaux and Related Methods (TABLEAUX)

作者： Lang, Timo UCL London England

ISBN: (纸本)9783031435126;9783031435133

We define two simple systems of rules, i.e. calculi with a global condition on the order of rule instances in a proof, for the modal logics of shift-reflexive and Euclidean frames respectively. Cutelimination, and therefore the subformula property, can be derived directly from the cut-elimination property of adjacent logics. We compare our system to the calculus of grafted hypersequents, which has previously been used to capture both logics. We then discuss an attempt to obtain similar 'modular' cut-elimination proofs in other systems of rules. This general attempt is carried out for two more logics, namely the modal logic of serial frames and the intermediate logic axiomatised by the law of the weak excluded middle.

关键词： Biomineralization

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Analysis of Frequency Bands in Electroencephalograms for Automatic Detection of Photoparoxysmal Responses 18th

Analysis of Frequency Bands in Electroencephalograms for Aut...

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18th International Conference on Hybrid artificial intelligence Systems

作者： Moncada Martins, Fernando Gonzalez, Victor M. Villar, Jose R. Garcia Lopez, Beatriz Isabel Gomez-Menendez, Ana Univ Oviedo Elect Engn Dept Oviedo Spain Univ Oviedo Comp Sci Dept Oviedo Spain Burgos Univ Hosp Neurophysiol Dept Burgos Spain

ISBN: (纸本)9783031407246;9783031407253

Photosensitivity is a neurological disorder in which a patient's brain produces epileptic discharges, known as Photoparoxysmal Responses (PPR), as a reaction to certain visual stimuli. The current standardized process of diagnosis used in hospitals consists of submitting the subject to Intermittent Photic Stimulation (IPS) process, attempting to trigger these phenomena. At the same time, the brain's electrical activity is recorded as an electroencephalogram (EEG) while the clinical specialists visually look for the PPRs provoked during the session if any. This research tests a modified version of a frequency-analysis-based method designed by neurophysiologists to predict epileptic activity during IPS by applying the Discrete Fourier Transform to extract spectral components and compare them. The goal is to detect these epileptic phenomena instead of predicting them by classifying EEG windows as PPR or not and flashing frequencies as PPR-triggering or not. The obtained results reveal that this new method cannot outperform those achieved from our previous research on automatic PPR detection. This research is performed with subjects at the Burgos University Hospital, Spain.

关键词： EEG Electroencephalography PPR Photoparoxysmal Response Machine Learning Photosensitivity Epilepsy

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A Comparative Study of Chatbot Response Generation: Traditional Approaches Versus Large Language Models 1

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16th International Conference on Knowledge Science, Engineering and Management (KSEM)

作者： McTear, Michael Marokkie, Sheen Varghese Bi, Yaxin Ulster Univ Belfast BT15 1ED North Ireland

ISBN: (数字)9783031402869

ISBN: (纸本)9783031402852;9783031402869

Chatbot responses can be generated using traditional rule-based conversation design or through the use of large language models (LLMs). In this paper we compare the quality of responses provided by LLM-based chatbots with those provided by traditional conversation design. The results suggest that in some cases the use of LLMs could improve the quality of chatbot responses. The paper concludes by suggesting that a combination of approaches is the best way forward and suggests some directions for future work.

关键词： Conversational AI Large Language Models Conversation design ChatGPT Bard Rasa

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Twin Graph Attention Network with Evolution Pattern Learner for Few-Shot Temporal Knowledge Graph Completion 1

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16th International Conference on Knowledge Science, Engineering and Management (KSEM)

作者： Liang, Yi Zhao, Shuai Cheng, Bo Yang, Hao Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol 10 Xitucheng Rd Beijing Peoples R China Guangxi Key Lab Cryptog & Informat Secur Guilin Peoples R China Huawei Technol Co Ltd Labs 2012 Beijing Peoples R China

ISBN: (数字)9783031402838

ISBN: (纸本)9783031402821;9783031402838

Recent years have witnessed a growing number of studies on few-shot knowledge graph completion (FSKGC), which aims to infer new facts for relations given its few-shot observed samples. Despite current research's great success in static knowledge graphs, few-shot temporal knowledge graph completion (FSTKGC) has not been well explored yet. Existing FSTKGC solutions mainly face two challenges. First, these models fail to distinguish the contribution of neighbors and model the difference between recurring and ever-changing facts. Second, they ignore the latent evolution patterns from observed temporal samples when learning relation representations. In this paper, we propose a novel framework named TwinGAT-VEDA with twin graph attention and an evolution pattern learner to address the above issues. First, our model devises two graph attention network (the twins) to aggregate most relative signals from recurring and dynamic neighbors separately and automatically fuses these futures based on the interaction between the subject and object. Secondly, we inject the time-differences to encode entity pairs and learn evolution patterns from few-shot reference sequence to represent few-shot relations. Comprehensive experiments on two benchmark datasets ICEWS-few-intp and GDELT-few-intp demonstrate that TwinGAT-VEDA achieves the state-of-the-art results.

关键词： Knowledge Graph Completion Link Prediction Temporal Knowledge Graph Few-Shot Learning Graph Learning Representation Learning

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AN Using Local Search in Multi-issue Bilateral and Repeated Negotiation 24th

AN Using Local Search in Multi-issue Bilateral and Repeated ...

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24th International Conference on Principles and Practice of Multi-Agent Systems (PRIMA)

作者： Haim, Galit Langer, Jonathan Yaniv, Raz Coll Management Acad Studies Rishon Leziyyon Israel

ISBN: (纸本)9783031212024;9783031212031

Designing an automated agent for Human-Agent Negotiation is a challenging task. Especially in the domain that combines multi-issue bilateral negotiations and repeated negotiations. In this domain, the agents negotiate with humans over more than one item, and there are several rounds of negotiation in each game. Designing this kind of agent can be very challenging. Our agent needs to estimate the preferences of the human opponent in real-time, proposing fair offers that will be excepted by the human opponent but taking into account, not proposing offers that don't increase the agent's score. On the other hand, local search algorithms have proven to be effective in a variety of fields in artificial intelligence. In this paper, we present a novel approach for an automated agent for this type of negotiation by using local search algorithms, in particular: Simulated Annealing and Hill Climbing. As we analyze the results from our experiments and compare the local search algorithms, we show that local search algorithms can be more efficient in Human-Agent negotiation than traditional methods. Moreover, our agent is capable of negotiating efficiently and outperforming the human opponent.

关键词： Human-agent negotiation Multi-issue bargaining Repeated negotiations Local search Simulated annealing Hill climbing

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RA-KD: Random Attention Map Projection for Knowledge Distillation 19th

RA-KD: Random Attention Map Projection for Knowledge Distill...

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19th International Conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Zhang, Linna Chen, Yuehui Cao, Yi Zhao, Yaou Univ Jinan Sch Informat Sci & Engn Jinan Peoples R China

ISBN: (纸本)9789819947515;9789819947522

Pretrained language models such as BERT, ELMO, and GPT have proven to be effective for natural language processing tasks. However, deploying them on computationally constrained devices poses a challenge. Moreover, the practical application of these models is affected by the training and deployment of large-scale pretrained language models. While knowledge distillation (KD) in the intermediate layer can improve standard KD techniques, especially for large-scale pretrained language models, distillation in the intermediate layers brings excessive computational burdens and engineering difficulties in mapping the middle layer of a student model with a variable number of layers. The attention map is one of the essential blocks in the intermediate layer. To address these problems, we propose an approach called random attention map projection, in which the intermediate layers are randomly selected from the teacher model. Then, the attention map knowledge is extracted to the student model's attention block. This approach enables the student model to capture deeper semantic information while reducing the computational cost of the intermediate layer distillation method. We conducted experiments on the GLUE dataset to verify the effectiveness of our approach. Our proposed RA-KD approach performs considerably better than other KD approaches in both performance and training time.

关键词： Model Compression Knowledge Distillation BERT

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Bi-Space Search: Optimizing the Hybridization of Search Spaces in Solving the One Dimensional Bin Packing Problem 21st

Bi-Space Search: Optimizing the Hybridization of Search Spac...

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21st International Conference on artificial intelligence and Soft Computing (ICAISC)

作者： Beckedahl, Derrick Pillay, Nelishia Univ Pretoria Pretoria South Africa

ISBN: (纸本)9783031234798;9783031234804

Search methodologies essentially explore a solution space to solve optimization problems. As the field has developed the effectiveness of exploring other spaces has been established. For example genetic programming explores the program space. Similarly, hyper-heuristics explore the heuristic space. In previous work the advantage of switching search between different spaces rather than working in a single space has been illustrated. This paper extends this work by presenting the bi-space search which optimizes when the switch between spaces should take place. The bi-space search employs iterated local search to optimize when to switch between the solution and heuristic spaces in solving discrete optimization problems. The performance of the bi-space search is compared to searching the solution space only and a hyper-heuristic searching the heuristic space. Both the solution and heuristic space searches employ iterated local search to explore the solution and heuristic space respectively. All three searches are evaluated on the one dimensional bin packing problem. The bi-space search was found to outperform the solution space search and hyper-heuristic.

关键词： Bi-space search Hyper-heuristic Solution space search One dimensional bin packing

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Optimistic Exploration Based on Categorical-DQN for Cooperative Markov Games 1

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4th International Conference on Distributed artificial intelligence (DAI)

作者： Tian, Yu Zhang, Chengwei Guo, Qing Zheng, Kangjie Fang, Wanqing Zhao, Xintian Zhang, Shiqi Heilongjiang Univ Sci & Technol Harbin Peoples R China Dalian Maritime Univ Dalian Peoples R China Nanyang Technol Univ Singapore Singapore

ISBN: (数字)9783031255496

ISBN: (纸本)9783031255496;9783031255489

In multiagent reinforcement learning (MARL), independent cooperative learners face numerous challenges when learning the optimal joint policy, such as non-stationarity, stochasticity, and relative over-generalization problems. To achieve multiagent coordination and collaboration, a number of works designed heuristic experience replay mechanisms based on the `optimistic' principle. However, it is difficult to evaluate the quality of an experience effectively, different treatments of experience may lead to overfitting and be prone to converge to sub-optimal policies. In this paper, we propose a new method named optimistic exploration categorical DQN (OE-CDQN) to apply the `optimistic' principle to the action exploration process rather than in the network training process, to bias the probability of choosing an action with the frequency of receiving the maximum reward for that action. OE-CDQN is a combination of the `optimistic' principle and CDQN, using an `optimistic' re-weight function on the distributional value output of the CDQN network. The effectiveness of OE-CDQN is experimentally demonstrated on two well-designed games, i.e., the CMOTP game and a cooperative version of the boat problem which confronts ILs with all the pathologies mentioned above. Experimental results show that OE-CDQN outperforms state-of-the-art independent cooperative methods in terms of both learned return and algorithm robustness.

关键词： Cooperative Markov games Distributional reinforcement learning Independent learning Optimistic principle

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