Event-related functional magnetic resonance imaging of reward-related brain circuitry in children and adolescents

BACKGROUND: Functional disturbances in reward-related brain systems are thought to play a role in the development of mood, impulse, and substance abuse disorders. Studies in non-human primates have identified brain regions, including the dorsal / ventral striatum and orbital-frontal cortex (OFC), in which neural activity is modulated by reward. Recent studies in adults have concurred with these findings by observing reward-contingent blood oxygen level dependant (BOLD) responses in these regions during functional magnetic resonance imaging (FMRI) paradigms. However no previous studies indicate whether comparable modulations of neural activity exist in the brain reward systems of children and adolescents. METHODS: We used event-related FMRI and a behavioral paradigm modeled on previous work in adults to study brain responses to monetary gains and losses in non-psychiatric children and adolescents as part of a program examining the neural substrates of anxiety and depression in youth. RESULTS: Regions and time-courses of reward-related activity were similar to those observed in adults with condition-dependent BOLD changes in the ventral striatum, lateral and medial OFC; specifically, these regions showed larger responses to positive than to negative feedback. CONCLUSIONS: These results provide further evidence for the value of event-related FMRI in examining reward systems of the brain, demonstrate the feasibility of this approach in children and adolescents, and establish a baseline from which to understand the pathophysiology of reward-related psychiatric disorders in youth

A Deep Learning Approach for Predicting Two-dimensional Soil Consolidation Using Physics-Informed Neural Networks (PINN)

Soil consolidation is closely related to seepage, stability, and settlement of geotechnical buildings and foundations, and directly affects the use and safety of superstructures. Nowadays, the unidirectional consolidation theory of soils is widely used in certain conditions and approximate calculations. The multi-directional theory of soil consolidation is more reasonable than the unidirectional theory in practical applications, but it is much more complicated in terms of index determination and solution. To address the above problem, in this paper, we propose a deep learning method using physics-informed neural networks (PINN) to predict the excess pore water pressure of two-dimensional soil consolidation. In the proposed method, (1) a fully connected neural network is constructed, (2) the computational domain, partial differential equation (PDE), and constraints are defined to generate data for model training, and (3) the PDE of two-dimensional soil consolidation and the model of the neural network is connected to reduce the loss of the model. The effectiveness of the proposed method is verified by comparison with the numerical solution of PDE for two-dimensional consolidation. Using this method, the excess pore water pressure could be predicted simply and efficiently. In addition, the method was applied to predict the soil excess pore water pressure in the foundation in a real case at Tianjin port, China. The proposed deep learning approach can be used to investigate the large and complex multi-directional soil consolidation.Comment: 23 page

ZRIGF: An Innovative Multimodal Framework for Zero-Resource Image-Grounded Dialogue Generation

Image-grounded dialogue systems benefit greatly from integrating visual information, resulting in high-quality response generation. However, current models struggle to effectively utilize such information in zero-resource scenarios, mainly due to the disparity between image and text modalities. To overcome this challenge, we propose an innovative multimodal framework, called ZRIGF, which assimilates image-grounded information for dialogue generation in zero-resource situations. ZRIGF implements a two-stage learning strategy, comprising contrastive pre-training and generative pre-training. Contrastive pre-training includes a text-image matching module that maps images and texts into a unified encoded vector space, along with a text-assisted masked image modeling module that preserves pre-training visual features and fosters further multimodal feature alignment. Generative pre-training employs a multimodal fusion module and an information transfer module to produce insightful responses based on harmonized multimodal representations. Comprehensive experiments conducted on both text-based and image-grounded dialogue datasets demonstrate ZRIGF's efficacy in generating contextually pertinent and informative responses. Furthermore, we adopt a fully zero-resource scenario in the image-grounded dialogue dataset to demonstrate our framework's robust generalization capabilities in novel domains. The code is available at https://github.com/zhangbo-nlp/ZRIGF.Comment: ACM Multimedia 2023 Accpeted, Repo: https://github.com/zhangbo-nlp/ZRIG

Prediction of the transaction confirmation time in Ethereum Blockchain

La blockchain propose un système d'enregistrement décentralisé, immuable et transparent. Elle offre un réseau de nœuds sans entité de gouvernance centralisée, ce qui la rend "indéchiffrable" et donc plus sûr que le système d'enregistrement centralisé sur papier ou centralisé telles que les banques. L’approche traditionnelle basée sur l’enregistrement ne fonctionne pas bien avec les relations numériques où les données changent constamment. Contrairement aux canaux traditionnels, régis par des entités centralisées, blockchain offre à ses utilisateurs un certain niveau d'anonymat en leur permettant d'interagir sans divulguer leur identité personnelle et en leur permettant de gagner la confiance sans passer par une entité tierce. En raison des caractéristiques susmentionnées de la blockchain, de plus en plus d'utilisateurs dans le monde sont enclins à effectuer une transaction numérique via blockchain plutôt que par des canaux rudimentaires. Par conséquent, nous devons de toute urgence mieux comprendre comment ces opérations sont gérées par la blockchain et combien de temps cela prend à un nœud du réseau pour confirmer une transaction et l’ajouter au réseau de la blockchain. Dans cette thèse, nous visons à introduire une nouvelle approche qui permettrait d'estimer le temps il faudrait à un nœud de la blockchain Ethereum pour accepter et confirmer une transaction sur un bloc tout en utilisant l'apprentissage automatique. Nous explorons deux des approches les plus fondamentales de l’apprentissage automatique, soit la classification et la régression, afin de déterminer lequel des deux offrirait l’outil le plus efficace pour effectuer la prévision du temps de confirmation dans la blockchain Ethereum. Nous explorons le classificateur Naïve Bayes, le classificateur Random Forest et le classificateur Multilayer Perceptron pour l’approche de la classification. Comme la plupart des transactions sur Ethereum sont confirmées dans le délai de confirmation moyen (15 secondes) de deux confirmations de bloc, nous discutons également des moyens pour résoudre le problème asymétrique du jeu de données rencontré avec l’approche de la classification. Nous visons également à comparer la précision prédictive de deux modèles de régression d’apprentissage automatique, soit le Random Forest Regressor et le Multilayer Perceptron, par rapport à des modèles de régression statistique, précédemment proposés, avec un critère d’évaluation défini, afin de déterminer si l’apprentissage automatique offre un modèle prédictif plus précis que les modèles statistiques conventionnels.Blockchain offers a decentralized, immutable, transparent system of records. It offers a peer-to-peer network of nodes with no centralised governing entity making it ‘unhackable’ and therefore, more secure than the traditional paper based or centralised system of records like banks etc. While there are certain advantages to the paper based recording approach, it does not work well with digital relationships where the data is in constant flux. Unlike traditional channels, governed by centralized entities, blockchain offers its users a certain level of anonymity by providing capabilities to interact without disclosing their personal identities and allows them to build trust without a third-party governing entity. Due to the aforementioned characteristics of blockchain, more and more users around the globe are inclined towards making a digital transaction via blockchain than via rudimentary channels. Therefore, there is a dire need for us to gain insight on how these transactions are processed by the blockchain and how much time it may take for a peer to confirm a transaction and add it to the blockchain network. In this thesis, we aim to introduce a novel approach that would allow one to estimate the time (in block time or otherwise) it would take for Ethereum Blockchain to accept and confirm a transaction to a block using machine learning. We explore two of the most fundamental machine learning approaches, i.e., Classification and Regression in order to determine which of the two would be more accurate to make confirmation time prediction in the Ethereum blockchain. More specifically, we explore Naïve Bayes classifier, Random Forest classifier and Multilayer Perceptron classifier for the classification approach. Since most transactions in the network are confirmed well within the average confirmation time of two block confirmations or 15 seconds, we also discuss ways to tackle the skewed dataset problem encountered in case of the classification approach. We also aim to compare the predictive accuracy of two machine learning regression models- Random Forest Regressor and Multilayer Perceptron against previously proposed statistical regression models under a set evaluation criterion; the objective is to determine whether machine learning offers a more accurate predictive model than conventional statistical models

Large Language Models Perform Diagnostic Reasoning

We explore the extension of chain-of-thought (CoT) prompting to medical reasoning for the task of automatic diagnosis. Motivated by doctors' underlying reasoning process, we present Diagnostic-Reasoning CoT (DR-CoT). Empirical results demonstrate that by simply prompting large language models trained only on general text corpus with two DR-CoT exemplars, the diagnostic accuracy improves by 15% comparing to standard prompting. Moreover, the gap reaches a pronounced 18% in out-domain settings. Our findings suggest expert-knowledge reasoning in large language models can be elicited through proper promptings.Comment: Accepted as a Tiny Paper at ICLR 2023 (10 pages, 5 figures