Miracle-dz/UniFAR-FTUs huggingface dataset:
Argument and explanation

In this paper, we bring together two closely related, but distinct, notions: argument and explanation. We clarify their relationship. We then provide an integrative review of relevant research on these notions, drawn both from the cognitive science and the artificial intelligence (AI) literatures. We then use this material to identify key directions for future research, indicating areas where bringing together cognitive science and AI perspectives would be mutually beneficial. This article is part of a discussion meeting issue ‘Cognitive artificial intelligence’.
Machine Learning for Interconnect Network Traffic Forecasting: Investigation and Exploitation

Interconnect networks play a key role in high-performance computing (HPC) systems. Parallel discrete event simulation (PDES) has been a long-standing pillar for studying large-scale networking systems by replicating the real-world behaviors of HPC facilities. However, the simulation requirements and computational complexity of PDES are growing at an intractable rate. An active research topic is to build a surrogate-ready PDES framework where an accurate surrogate model built on machine learning can be used to forecast network traffic for improving PDES. In this paper, we make the first attempt to introduce two representative time series methods, the Autoregressive Integrated Moving Average (ARIMA) and the Adaptive Long Short-Term Memory (ADP-LSTM), to forecast the traffic in interconnect networks, using the Dragonfly system as a representative example. The proposed ADP-LSTM can efficiently adapt to the ever-changing network traffic, facilitating the forecasting capability for intricate network traffic, by incorporating a novel online learning strategy. Our preliminary analysis demonstrates promising results and shows that ADP-LSTM can consistently outperform ARIMA with significantly less time overhead.
Analysis of Fluid-Structu re Interaction on Moving Airfoils by Means of an Improved ALE-method

This paper presents a computational analysis of fluid-structure interaction. A staggered approach is adopted where the fluid and structure are integrated in time by separate solvers. The interaction is taken into account by the boundary conditions. The unsteady compressible flow is calculated by a two dimensional Euler and Navier-Stokes solver on unstructured grids. This solver is extended to moving domain problems by the Arbitrary Euler-Lagrange (ALE) method. The mesh is then deformed by an improved spring analogy which allows for large deformations of the computational grid. The influence of the structural time integration is investigated by several methods. The staggered approach is modified by a subcycling algorithm where the time steps for the fluid and structure are different. The capabilities of the improved spring analogy are shown by a large rotation of the NACA0012 airfoil Furthermore, numerical results are shown for the forced pitching vibration of the NACA0012 airfoil and aero-elasticity of the same airfoil supported by elastic springs.
Query Context Expansion for Open-Domain Question Answering

Humans are accustomed to autonomously associating prior knowledge with the text in a query when answering questions. However, for machines lacking cognition and common sense, a query is merely a combination of some words. Although we can enrich the semantic information of the given query through language representation or query expansion (QE), the information contained in the query is still insufficient. In this paper, we propose an effective passage retrieval method named query context expansion-based retrieval (QCER) for open-domain question answering (OpenQA). QCER associates a query with domain information by adding contextual association information based on the pseudo-relevance feedback (PRF). QCER uses a dense reader to select top-n expansion terms for QE. We implement QCER by appending reader predictions, theoretically present in candidate passages, as contextual information to the initial query to form the new query. QCER with sparse representations (BM25) can improve retrieval efficiency and accelerate query convergence so that the reader can find the desired answer using fewer relevant passages, e.g., 10 passages, as soon as possible. Moreover, QCER can be easily combined with dense passage retrieval (DPR) to achieve even better performance, as sparse and dense representations are often complementary. Remarkably, we demonstrate that QCER achieves state-of-the-art performance in three tasks, passage retrieval, passage reading, and passage reranking, on the Natural Questions (NQ) and TriviaQA (Trivia) datasets under an extractive QA setup.
Towards accessible Parallel Discrete Event Simulation of Spiking Neural Networks

Spiking Neural Networks (SNNs) are a class of Artificial Neural Networks that closely mimic biological neural networks. Their potential to advance medical and artificial intelligence research makes them particularly interesting to study. Since their behaviour cannot be computed with single one-shot functions, simulations are employed to study their evolution over time. Recent works presented the possibility of simulating SNNs using speculative Parallel Discrete Event Simulation (PDES). However, no high-level interface to run SNN simulations using PDES was provided, leaving the model implementation to the users. This demanding process creates a barrier to the adoption of the method. In this work, the initial efforts towards making PDES-based simulation of SNNs easily accessible via interfaces with a high abstraction level (PyNN) are reported. Preliminary performance results are reported and comparisons are made between PDES using the ROme OpTimistic Simulator (ROOT-Sim), and the state-of-the-art SNN simulator NEST, both used through the PyNN interfaces.
Decision Model of Ship Intelligent Collision Avoidance Based on Automatic Information System Data and Generic Adversary Imitation Learning-Deep Deterministic Policy Gradient

Aiming at the problems that the current decision-making model of ship collision avoidance does not consider International Regulations for Preventing Collisions at Sea (COLREGS), ship maneuverability, and the need for a lot of training time, combined with the advantages of reinforcement learning and imitation learning, a ship intelligent collision avoidance decision-making model based on Generic Adversary Imitation Learning (GAIL) is proposed: Firstly, the collision avoidance data in Automatic Information System (AIS) data is extracted as expert data; Secondly, in the generator part, the environment model is established based on Mathematical Model Group (MMG) and S-57 chart rendering, and the state space, behaviour space and reward function of reinforcement learning are constructed. The deep deterministic policy gradient (DDPG) is used to interact with the environment model to generate ship trajectory data. At the same time, the generator can constantly learn expert data; Finally, a discriminator can distinguish the expert data from the data generated by the generator is constructed and trained. The model training is completed when the discriminator cannot distinguish the two. In order to verify the performance of the model, AIS data near the South China Sea is used to process and extract collision avoidance decision data, and a ship intelligent collision avoidance decision model based on GAIL is established. After the model converges, the final generated data is compared with the expert data. The experimental results verify that the model proposed in this paper can reproduce the expert collision avoidance trajectory and is a practical decision model of ship collision avoidance.
Research on energy consumption prediction based on fast attribute reduction of weighted neighborhood rough set with moving horizon

In actual prediction scenarios, attribute features include time data, weather data, and energy consumption data. The relationship between attribute features is very complex. Exploring the relationship between feature attributes and decision sets with fast attribute reduction can reduce the amount of model training data. Since seasonal and temporal variations greatly influence weather and energy consumption data, the moving horizon method is used to update the features and improve the accuracy of energy consumption prediction.From above, an energy consumption prediction model based on fast attribute reduction of weighted neighborhood rough set with moving horizon long short-term memory neural network(LSTM) is proposed in this paper. In the experiment of predicting the actual energy consumption of a building, the model evaluation results show that 20% of training data is reduced at the expense of 0.4% classification accuracy. Compared with the traditional non-rolling method, the Root Mean Square Error (RMSE) of the moving horizon LSTM prediction method is reduced by 33.08% on average, and the training speed is increased by 5.25% on average. The prediction effect is better. Therefore, this prediction model can be used to predict building energy consumption quickly and accurately and has strong robustness and generalization ability, which provides the theoretical basis and method support for fine management of building energy consumption, building energy conservation, and emission reduction.
Research on License Plate Recognition Based on Deep Learning in Complex Scenarios

The license plate angle is unfixed, the vehicle position is ununiform, and the picture is not sufficiently illuminated which leads to the decrease of license plate recognition accuracy. In order to improve the accuracy of license plate recognition, a deep learning-based license plate recognition method is proposed. For license plate location problem, YOLOv3 algorithm is used. The algorithm is more capable of recognizing small targets and is suitable for license plate location recognition that requires precise positioning. For the problem of license plate character recognition, the CNN plus multitask recognition method is proposed for recognition. The experimental results show that the accuracy of the license plate recognition method proposed in this paper reaches 96%, and the intelligent license plate recognition is realized.
Pengenalan Aksara Bali Dengan Metode Local Binary Pattern

Abstrak Aksara Bali merupakan salah satu tulisan daerah yang digunakan di Indonesia. Untuk membaca aksara Bali tidaklah mudah. Hal ini disebabkan karena kesulitan untuk mengenali bentuk atau pola dari suku kata dasar aksara tersebut. Suku kata dasar aksara Bali hampir memiliki struktur yang sama, sehingga di dalam pembacaannya akan menemukan kesulitan mengidentifikasi suku kata dasar. Penelitian ini dibuat dengan tujuan untuk membuat suatu sistem yang mampu mengenali pola dari aksara Bali dimana aksara Bali yang digunakan adalah aksara Bali dasar. Sistem ini menggunakan teknik Local Binary Pattern (LBP) untuk ekstraksi cirinya. Salah satu sifat paling penting dari operator LBP ialah kesederhanaan perhitungannya, memiliki waktu komputasi yang lebih cepat, dan sifatnya yang invarian terhadap perubahan fotometri dari objek yang sama, dikarenakan LBP merupakan ukuran intensitas relatif suatu piksel dengan intensitas piksel disekitarnya. LBP didefinisikan sebagai perbandingan nilai piksel pada pusat citra dengan nilai piksel disekelilingnya sehingga diperoleh nilai biner pada matriks tersebut. Hasil dari LBP ini dijadikan masukan pada proses klasifikasi citra yang menggunakan Support Vector Machine dan K-Nearest Neighbor. Keunggulan dari metode KNN adalah tangguh terhadap data latih yang memiliki banyak noise dan efektif apabila data latihnya berukuran besar, sedangkan metode klasifikasi SVM mempunyai beberapa kelebihan, diantaranya bisa memodelkan dan mengklasifikasikan hubungan antar variabel tanpa perlu asumsi yang ketat, efisien, dan interpretasinya mudah. Pada penelitian ini juga membandingkan proses klasifikasi antara metode Support Vector Machine dengan K- Nearest Neighbor. Hasil dari simulasi yang dilakukan sistem dapat mengenali Aksara Bali dengan tingkat akurasi tertinggi adalah 74,6%, ddengan waktu komputasi rata-rata sistem sebesar 2,3203 detik. Kata kunci : Aksara Bali, Local Binary Pattern (LBP), Support Vector Machine (SVM), K-Nearest Neighbor (KNN).
A review of wildfire impacts on stream temperature and turbidity across scales

Wildfire has increased in severity and frequency with climate change and human activities in recent years, threatening water-related ecosystem services. Forested watersheds are at risk of impacts of wildfires that alter land cover, and hydrological processes, and influence drinking water quality and aquatic habitat. To date, most research on post-fire hydrologic effects has focused on water quantity, while stream temperature and turbidity received less attention. In this study, we reviewed 62 articles to examine wildfire drivers and processes associated with turbidity and stream temperature behavior through a geographic lens in the context of ecosystem services. Our goals were to (1) evaluate drivers of post-fire changes in turbidity and stream temperature; (2) examine mechanisms and processes responsible for spatial and temporal variabilities of changes; and (3) address scale-dependent knowledge gaps to recommend future research directions. Positive correlations between turbidity changes following wildfire were heavily influenced by fire severity, forest diversity, and landscape alterations by human activities such as salvage logging. Stream temperature increases result from loss of riparian canopy cover and decreased shading, but they were highly site-specific and dependent on topographic variations. We attribute variabilities in our findings to climate variability and heavy disparity across spatial and temporal scales when assessing the direction and magnitude of post-fire impacts. Future research should incorporate more long-term rigorous monitoring efforts and spatiotemporally explicit models to better represent the complex post-fire hydrologic system that influences water quality.
KDM3A and KDM3B Maintain Naïve Pluripotency Through the Regulation of Alternative Splicing

Histone modifying enzymes play a central role in maintaining cell identity by establishing a conducive chromatin environment for lineage specific transcription factor activity. Pluripotent embryonic stem cell (ESC) identity is characterized by a lower abundance of gene repression associated histone modifications that enables rapid response to differentiation cues. The KDM3 family of histone demethylases removes the repressive histone H3 lysine 9 dimethylation (H3K9me2). Here we uncover a surprising role for the KDM3 proteins in the maintenance of the pluripotent state through post-transcriptional regulation. We find that KDM3A and KDM3B interact with RNA processing factors such as EFTUD2 and PRMT5. Acute selective degradation of the endogenous KDM3A and KDM3B proteins resulted in altered splicing independent of H3K9me2 status or catalytic activity. These splicing changes partially resemble the splicing pattern of the more blastocyst-like ground state of pluripotency and occurred in important chromatin and transcription factors such as Dnmt3b, Tbx3 and Tcf12. Our findings reveal non-canonical roles of histone demethylating enzymes in splicing to regulate cell identity.
A comparison of mesophotic and shallow sponge holobionts resilience to predicted future temperature elevation

Climate change is predicted to have detrimental impacts on sessile invertebrates, including sponges. Mesophotic ecosystems have been suggested to play a major role as refugia for coral reef sponge species, however knowledge regarding the ability of mesophotic sponges to cope with thermal stress is scarce. In this study we compared the response of the sponge Diacarnus erythraeanus, a widespread Red Sea sponge, from the shallow and mesophotic reef, to moderate and acute temperature elevation (2°C and 6°C, respectively) for short and long term periods (two and 35 days, respectively) by measuring physiological parameters (respiration, oxygen removal, pumping rates, and photosynthetic efficiency), and the microbiome composition change. The results indicated that mesophotic and shallow populations of D. erythraeanus are highly tolerant to both moderate and acute heat stress, demonstrating a high survival rate (100%) across the experimental treatments, with no visible signs of bleaching or necrosis. Exposure to heat stress resulted in significant alterations in the physiological parameters of sponges, including higher respiration rate and lower photosynthetic efficiency. These alterations were accompanied by correspondingly significant microbial adjustments, thus emphasizing the essential role of the microbiome in the host’s ability to persist when facing essential environmental stress. Moreover, while shallow and mesophotic sponges showed similar physiological tolerance to heat stress, their microbial response differed: while the microbiome diversity of the mesophotic sponges remained stable throughout the experiment, the shallow one significantly changed. This result suggests that their underlying coping mechanisms might differ between mesophotic and shallow populations. Since the associated-microbiome is largely regulated by the sponge-host genetics, difference in microbial adjustments to stress between populations, could indicate genetic variability between hosts. Therefore, while the results of this study support the hypothesis that mesophotic coral reefs could serve as thermal refugia for some sponge species, it raises the question regarding the validity of MCEs as a refuge for shallow populations. Finally, it emphasizes the crucial need to elucidate the underlying mechanisms governing the sponge-microbiome interactions, specifically in the context of the anticipated climate change scenarios.
Paths towards parenthood after repeated treatment failures: a comparative study on predictors of psychological health outcomes in infertile couples persisting in treatments or opting for adoption

Introduction Infertility literature suggests widespread recourse to long-term medical treatments despite evidence of high stress, costs, and adverse effects of repeated treatment failures. However, there is a lack of research comparing predictors of stress and psychological health outcomes between members of infertile couples who – after repeated failures – persist in pursuing medical treatments (PT) with those who opted for quitting treatments and adopting (QTA). Basing on a transactional and multidimensional approach to infertility-related stress and health, the present study aims at exploring individual (socio-demographics; coping strategies) and situational (infertility-related parameters; infertility-related stressors; couple’s dyadic adjustment dimensions) predictors of state-anxiety and depression in male and female partners of PT-infertile couples and of QTA-infertile couples. Methods Participants were both members of 176 couples with duration of infertility and a history of medical treatments for at least 3 years (76 PT-infertile couples, 100 QTA-infertile couples). The study variables were compared by study group across genders. Structural equation models (SEM) were used to test main and moderating effects of study variables on state-anxiety and depression by study group and across genders. Results Members of infertile couples quitting treatments and adopting (QTA) reported significantly lower levels of state-anxiety and depression, higher stress related to need for parenthood and rejection of childfree-lifestyle and lower stress related to social and couple’s relationship concerns than those who persist in pursuing medical treatments (PT). Members of infertile couples quitting treatments and adopting (QTA) recurred to a greater extent to active coping strategies (problem-solving/social-support) and to a lower extent to passive coping strategies (avoiding/turning-to-religion), and they reported higher levels of dyadic adjustment. Specificities in main and moderating factors related to state-anxiety and depression by study group and across genders were found. Conclusion Findings should be addressed to provide a comprehensive assessment of both members of infertile couples facing repeated treatment failures to identify risks and resources and develop tailored evidence-based interventions.
Alternation preferences affect focus marking in German and English differently

This study investigates the interplay between alternation preferences and corrective focus marking in the production of German and English speakers. Both languages prefer an alternation of strong and weak, and both use pitch accenting to indicate focus structure. The objective of the study is to determine whether the preference for rhythmic alternation can account for variations in the prosodic marking of focus. Contrary to previous claims, the results obtained from three production experiments indicate that rhythmic adjustment strategies do occur during focus marking. However, despite the similarities between the two languages, they employ different strategies when alternation and focus marking work in opposite directions. German speakers often employ a melodic alternation of high and low by realizing the first of two adjacent focus accents with a rising pitch accent (L*H), while English speakers frequently omit the first focus accent in clash contexts. This finding is further supported by a second experiment that investigates pitch accent clashes in rhythm rule contexts under various focus environments. The findings suggest that the preference for alternation can influence the prosodic marking of focus and contributes to variation in the realization of information-structure categories.
An evolution strategy of GAN for the generation of high impedance fault samples based on Reptile algorithm

In a distribution system, sparse reliable samples and inconsistent fault characteristics always appear in the dataset of neural network fault detection models because of high impedance fault (HIF) and system structural changes. In this paper, we present an algorithm called Generative Adversarial Networks (GAN) based on the Reptile Algorithm (GANRA) for generating fault data and propose an evolution strategy based on GANRA to assist the fault detection of neural networks. First, the GANRA generates enough high-quality analogous fault data to solve a shortage of realistic fault data for the fault detection model’s training. Second, an evolution strategy is proposed to help the GANRA improve the fault detection neural network’s accuracy and generalization by searching for GAN’s initial parameters. Finally, Convolutional Neural Network (CNN) is considered as the identification fault model in simulation experiments to verify the validity of the evolution strategy and the GANRA under the HIF environment. The results show that the GANRA can optimize the initial parameters of GAN and effectively reduce the calculation time, the sample size, and the number of learning iterations needed for dataset generation in the new grid structures.